Evidence claim

Relevance of ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) to "Select a copy-number variant detection and characterisation workflow"

Use case: Select a copy-number variant detection and characterisation workflow
Assessed by: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3)
Relevance: Proxy evidence
Endpoint: Accuracy, sensitivity, specificity, PPV, FPR, F1 and MCC of AnnotSV, ClassifyCNV, dbCNV, MarCNV-ISV and X-CNV against expert ACMG classifications

Proxy: automated pathogenicity classification of already detected CNVs bears on the characterisation and analyst-assessment part of the question, but no detection from sequencing reads is evaluated and the reference is expert ACMG labelling.

5 reviewed evaluations

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

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Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Citation locations 1 values source id cnv-20261011-zhang2026-source-article locator Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison group zhang2026-cnv-classification Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison title Automated ACMG CNV classification tools against expert labels (Zhang et al. 2026) Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Constraints 2 values Inspect every linked evaluation's source locator before citing a result. Do not combine this mapping's evaluations with detection results from other protocols; the endpoint is classification, not detection. Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.constraints` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Endpoint Accuracy, sensitivity, specificity, PPV, FPR, F1 and MCC of AnnotSV, ClassifyCNV, dbCNV, MarCNV-ISV and X-CNV against expert ACMG classifications Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.endpoint` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field links:assessed_by:cnv-20261011-zhang2026-protocol-five-category Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Headline metric accuracy Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.headline_metric` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Limitations 6 values How the five-class metrics are averaged across classes is not stated. Reference labels are expert ACMG classifications, a semi-quantitative standard with known inter-reviewer variability (Discussion paragraph 5). The set is enriched for pregnancy-loss and prenatal cases and is imbalanced (benign is about half of the duplications), which the authors say affects the metrics (Discussion paragraphs 5 and 6). Classification of already detected CNVs from their coordinates; no read data or detection step is evaluated. Some training sets of the evaluated tools (ClinVar, ClinGen, DGV) may overlap the published cohorts used as reference; the paper does not test this. Two of the three authors (X. Zhang and D. Chen) are authors of dbCNV (reference 24), developed in the same laboratory, so the dbCNV rows are author_reported. dbCNV's original test set was that laboratory's internal dataset, and this benchmark includes the laboratory's internal CNV-seq cases (December 2019 to November 2025); overlap is not tested. The paper declares no competing interests. Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.limitations` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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attributes.metric_qualifier attributes.printed_value attributes.numeric_value sha256 24e0d80f208f72dfaaef5aa6082d860868a6e3ace3db508bb4d09ae852f93ac3 record id cnv-20261011-zhang2026-result-five-category-x-cnv-specificity fields status attributes.metric attributes.metric_qualifier attributes.printed_value attributes.numeric_value sha256 f6d5dc3550583f8f18a5fe543369771e1624b82429341af3319935b17aa4db43 record id cnv-20261011-zhang2026-source-article fields status attributes.artifact_sha256 sha256 0e820fd41bddc8e50da7eda300178b6975ae8e3a43214db18cabd9fe1ab836a6 record id use-case-cnv-detection-characterisation fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0 Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Rationale Proxy: automated pathogenicity classification of already detected CNVs bears on the characterisation and analyst-assessment part of the question, but no detection from sequencing reads is evaluated and the reference is expert ACMG labelling. Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics' Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.rationale` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

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Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Original source · Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML

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Technical metadata and extraction receipts

Stable ID: use-case-mapping-cnv-20261011-zhang2026-five-category

field: links:assessed_by:cnv-20261011-zhang2026-protocol-five-category
value: cnv-20261011-zhang2026-protocol-five-category
relevance: proxy
endpoint: Accuracy, sensitivity, specificity, PPV, FPR, F1 and MCC of AnnotSV, ClassifyCNV, dbCNV, MarCNV-ISV and X-CNV against expert ACMG classifications
rationale: Proxy: automated pathogenicity classification of already detected CNVs bears on the characterisation and analyst-assessment part of the question, but no detection from sequencing reads is evaluated and the reference is expert ACMG labelling.
constraints: Inspect every linked evaluation's source locator before citing a result.; Do not combine this mapping's evaluations with detection results from other protocols; the endpoint is classification, not detection.
limitations: How the five-class metrics are averaged across classes is not stated.; Reference labels are expert ACMG classifications, a semi-quantitative standard with known inter-reviewer variability (Discussion paragraph 5).; The set is enriched for pregnancy-loss and prenatal cases and is imbalanced (benign is about half of the duplications), which the authors say affects the metrics (Discussion paragraphs 5 and 6).; Classification of already detected CNVs from their coordinates; no read data or detection step is evaluated.; Some training sets of the evaluated tools (ClinVar, ClinGen, DGV) may overlap the published cohorts used as reference; the paper does not test this.; Two of the three authors (X. Zhang and D. Chen) are authors of dbCNV (reference 24), developed in the same laboratory, so the dbCNV rows are author_reported. dbCNV's original test set was that laboratory's internal dataset, and this benchmark includes the laboratory's internal CNV-seq cases (December 2019 to November 2025); overlap is not tested. The paper declares no competing interests.
citation locators: source id: cnv-20261011-zhang2026-source-article; locator: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics'
source locator: cnv-20261011-zhang2026-source-article: Table 3; Methods 'Data analysis'; Results 'Comparative analysis of clinical metrics'
headline metric: accuracy
comparison group: zhang2026-cnv-classification
comparison title: Automated ACMG CNV classification tools against expert labels (Zhang et al. 2026)
stratum label: Five-category classification
stratum order: 1
reason: Recorded from the 2026-10-11 use-case literature pass (data/omics/use-case-coverage-cnv-20261011-zhang2026). Independently reviewed in docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md.
review: method: source-hash-verification; independent-cell-check; automated-source-review; scope-review; reviewer: claude; reviewer note: Independent Claude review agent reviewer-scnv; did not extract this batch; no human review claimed; date: 2026-10-12; artifact sha256: a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13018948/fullTextXML; method note: Drafted by the collector agent uc-splicing-cnv from the pinned source.; note: Checked by reviewer-scnv against the pinned source; verdict in docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md and in this batch's review.md.
reviewed evaluations: cnv-20261011-zhang2026-eval-five-category-annotsv; cnv-20261011-zhang2026-eval-five-category-classifycnv; cnv-20261011-zhang2026-eval-five-category-dbcnv; cnv-20261011-zhang2026-eval-five-category-marcnv-isv; cnv-20261011-zhang2026-eval-five-category-x-cnv
pins: record id: cnv-20261011-zhang2026-eval-five-category-annotsv; fields: status; links; attributes.origin; attributes.comparison; sha256: bd8c42b52c3aecd88bcf35744c951501becbc2b0510b3ca2a8c8448f86e66f78; record id: cnv-20261011-zhang2026-eval-five-category-classifycnv; fields: status; links; attributes.origin; attributes.comparison; sha256: 17fa498a13f490b0430829cc79c0ecb22ef447c0a266f4cc8eb67ae36e70cbcc; record id: cnv-20261011-zhang2026-eval-five-category-dbcnv; fields: status; links; attributes.origin; attributes.comparison; sha256: bf3b0ee7e7eaacd5f0134e3548fd6f692d5819f31f710fc2d1d445ead3f278b3; record id: cnv-20261011-zhang2026-eval-five-category-marcnv-isv; fields: status; links; attributes.origin; attributes.comparison; sha256: 4638b811bf0c72ef7722f11f85ff06763fd019f25da8628a8d2a0b9b8970f6ef; record id: cnv-20261011-zhang2026-eval-five-category-x-cnv; fields: status; links; attributes.origin; attributes.comparison; sha256: a32aac16571bfe5b82606b147006f914823ff9083cfa928b5b3415775a563d39; record id: cnv-20261011-zhang2026-protocol-five-category; fields: status; name; description; facets; source_ids; links; attributes; sha256: 806315da69bc4350ed827b9d7d68af758aa4abe172d13f3a07bdca2f284ff9e8; record id: cnv-20261011-zhang2026-result-five-category-annotsv-accuracy; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 2f9230a1f75fe95cd628e5bea388ff3745e4904a470084e58cb5277d5b05bb9d; record id: cnv-20261011-zhang2026-result-five-category-annotsv-f1-score; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 9b216273aaa7afd6bff97d1cf9a07acec2131fc3411c3e610e8125ac6788d2bd; record id: cnv-20261011-zhang2026-result-five-category-annotsv-false-positive-rate; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: be5cd139771f5ffe4ad0784b8587baab06128b50f04523aff3665a963a835b77; record id: cnv-20261011-zhang2026-result-five-category-annotsv-matthews-correlation-coefficient; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: f53fd332f2301cd451372347a66e9a5dda101dbb3b79266b698645e464ab6ac0; record id: cnv-20261011-zhang2026-result-five-category-annotsv-precision; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: ea16862d623c0da051de09fa41573a07fec8611e517400ba0f70b1c0c8b9c17f; record id: cnv-20261011-zhang2026-result-five-category-annotsv-recall; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: bc6ff8310a2551cf66a0d49c3ccf6214bdca3940f3c358e58ed847577d7c2953; record id: cnv-20261011-zhang2026-result-five-category-annotsv-specificity; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: d05f4891eb683cc81ccc5b30814601ca1cad884f0ad9db5feb9717bff4949cf9; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-accuracy; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: a98e60a8287bbfa02cab33757a548a8b451c421b420a1297dd0ea17d5977b9d2; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-f1-score; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: b90f81cdc75195b6629aaa2afc790ebbfba2ef596ac7b2d2aa630737b3776ade; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-false-positive-rate; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 273f953c448f06817b5281bdfa71b8a1b56c013d696056d9f202c232e9c04c83; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-matthews-correlation-coefficient; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: bea94fb80d7a46420e69af00f7c288adbd321285203afe0db2e449b6c9d0cae3; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-precision; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 860902cc2576f2f5e0679d2bdb70fe2e90703dd58fa8b90ea489d636239d8d79; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-recall; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: c0140e2e6cf0ff27c489802437f62a66adaf248038b81209b363d5c889807ff7; record id: cnv-20261011-zhang2026-result-five-category-classifycnv-specificity; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 56955eeec55e9af1b58486f41f0724ca439cf16bf3e7870a9623a469eae08309; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-accuracy; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 1f77a6d72d12d7e943287a96dbd233cfb07466eeb0dbe92de4393183d3d0cd03; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-f1-score; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 8601cfcfac854362caff1ad144955b08d82e5edf24f824a596812f723cde0583; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-false-positive-rate; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 3a3bc8f3dd18df67da786bc74e610c7a60387dc29cf932af7ffb48e9690d9946; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-matthews-correlation-coefficient; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: d7c7f9752bdcfa70c26135fb8bdb4c57abee7de2d6a4951cc4da0e2c366e27cf; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-precision; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: c210d1b37c5ea65b2d9cd3d672e5832a36adb3b5623defdae495a4ac9d5bf270; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-recall; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 6585b1912abc0083913f2bff4ff1b3db94cc61f7372910db31def06f6e6652e5; record id: cnv-20261011-zhang2026-result-five-category-dbcnv-specificity; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: a42435be8c37126efb01b99e355a9f5ad0feb086100e00ad540d3d20b6b9c154; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-accuracy; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 6d082ddd6fd1fd4259ee09c0aab680b16d62d116d3f74c84caead3066856584b; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-f1-score; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: a2f07f62b34fdf4db6becd3a62d1221a89f6f9e9f526556d15865672444175a6; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-false-positive-rate; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: a342aed304009537e920b4cf45f568f1726d6a078d497d30ff0f47137c07703a; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-matthews-correlation-coefficient; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: d389e0b73cfef11b3e973671be54a76f4533beb8cf197b7c883925aaf4ae791a; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-precision; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 3aa294fcd29cd39c0791ceb3fcef63f771a2b1b259a8e7a0a84e2a184ede9d9e; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-recall; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: c62e2e69611bd3677158b8b5df6e77fc3d9104a63a4f6ca717d9aa24102efccc; record id: cnv-20261011-zhang2026-result-five-category-marcnv-isv-specificity; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 38a008bbff595df0942ed1ae301ddfa7f4632f270d2cd738d690dde6cdcbe74c; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-accuracy; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: be2d3b5b503ee353a9ffb1d748cd97904bb0cd8a4aeeebdd86c0c8f0cfde97f9; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-f1-score; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: a3b55a8b44af6ddcbda81f275a11098ebecc4a4fd4ff8a93804aa4ab41fe092c; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-false-positive-rate; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 749688aba285b0f10911183164b6f74325f407c0fdd5c120f986b2535896d58a; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-matthews-correlation-coefficient; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 9be9e8b7f22a696de9a33e19f050029d03bf9bd6edeef0bbd38cc6e92d850c97; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-precision; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 361ec4bcdeb63785985fbc254092badf3d08dd6f3f75e0512b42727f6433a430; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-recall; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: 24e0d80f208f72dfaaef5aa6082d860868a6e3ace3db508bb4d09ae852f93ac3; record id: cnv-20261011-zhang2026-result-five-category-x-cnv-specificity; fields: status; attributes.metric; attributes.metric_qualifier; attributes.printed_value; attributes.numeric_value; sha256: f6d5dc3550583f8f18a5fe543369771e1624b82429341af3319935b17aa4db43; record id: cnv-20261011-zhang2026-source-article; fields: status; attributes.artifact_sha256; sha256: 0e820fd41bddc8e50da7eda300178b6975ae8e3a43214db18cabd9fe1ab836a6; record id: use-case-cnv-detection-characterisation; fields: status; name; description; facets; attributes.question; attributes.decision; attributes.inputs; attributes.output; attributes.exclusions; attributes.setting; attributes.clinical_scope; attributes.intended_users; sha256: e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0

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