From Diagnostic Accuracy to Accurate Diagnosis: Interpreting a Test Result with Confidence
Bibliographic record
Abstract
BACKGROUND: The Standard for Reporting of Diagnostic Accuracy statement promotes the reporting of confidence intervals (CIs) for indices of diagnostic test accuracy. However, these indices must be combined with an estimate of pretest probability to properly interpret the results of such tests, thus yielding positive and negative predictive values. For small sample sizes, CI estimation for predictive values based on the classical logit transformation has been found to be very conservative. A method based on computer simulation has therefore been suggested as an alternative. METHODS: ACI procedure for predictive values that yields limits completely contained in those provided by the logit transformation is proposed and evaluated. RESULTS: The proposed approach to CI construction maintains nominal coverage very well even when sample sizes are small. CONCLUSION: Accurate CIs for positive and negative predictive values can be obtained without using computer simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.578 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".