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Record W4384557485 · doi:10.1503/cmaj.221802

The association of sensitivity and specificity with disease prevalence: analysis of 6909 studies of diagnostic test accuracy

2023· article· en· W4384557485 on OpenAlexvenueno aff
M. Hassan Murad, Lifeng Lin, Haitao Chu, Bashar Hasan, Reem Alsibai, Alzhraa Salah Abbas, Reem A. Mustafa, Zhen Wang

Bibliographic record

VenueCanadian Medical Association Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
FundersU.S. National Library of MedicineNational Institute of Mental Health
KeywordsQuartileConfidence intervalMedicineMeta-analysisOdds ratioDiagnostic odds ratioLogistic regressionInternal medicineBivariate analysisRandom effects modelStatisticsMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Sensitivity and specificity are characteristics of a diagnostic test and are not expected to change as the prevalence of the target condition changes. We sought to evaluate the association between prevalence and changes in sensitivity and specificity. METHODS: We retrieved data from meta-analyses of diagnostic test accuracy published in the Cochrane Database of Systematic Reviews (2003-2020). We used mixed-effects random-intercept linear regression models to evaluate the association between prevalence and logit-transformed sensitivity and specificity. The model evaluated all meta-analyses as nested within each systematic review. RESULTS: We analyzed 6909 diagnostic test accuracy studies from 552 meta-analyses that were included in 92 systematic reviews. For sensitivity, compared with the lowest quartile of prevalence, the second, third and fourth quartiles were associated with significantly higher odds of identifying a true positive case (odds ratio [OR] 1.17, 95% confidence interval [CI] 1.09-1.26; OR 1.32, 95% CI 1.23-1.41; OR 1.47, 95% CI 1.37-1.58; respectively). For specificity, compared with the lowest quartile of prevalence, the second, third and fourth quartiles were associated with significantly lower odds of identifying a true negative case (OR 0.74, 95% CI 0.69-0.80; OR 0.65, 95% CI 0.60-0.70; OR 0.47, 95% CI 0.44-0.51; respectively). Pooled regression coefficients from bivariate models conducted within each meta-analysis showed that prevalence was positively associated with sensitivity and negatively associated with specificity. Findings were consistent across subgroups. INTERPRETATION: In this large sample of diagnostic studies, higher prevalence was associated with higher estimated sensitivity and lower estimated specificity. Clinicians should consider the implications of disease prevalence and spectrum when interpreting the results from studies of diagnostic test accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.067
metaresearch head score (Gemma)0.228
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.228
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0140.045
Bibliometrics0.0180.017
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.351
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations77
Published2023
Admission routes1
Has abstractyes

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