Bibliographic record
Abstract
Clinicians frequently confront challenges when using diagnostic tests to help them decide whether the patient before them suffers from a particular target condition or diagnosis. The primary issues to consider when determining the validity of a diagnostic test study are how the authors assembled the patients and whether they used an appropriate reference standard in all patients to determine whether the patients did or did not have the target condition. Surgeons should be interested in the characteristics of the test that indicates the direction and magnitude of change in the probability of the target condition associated with a particular test result. The likelihood ratio best captures the link between the pretest probability of the target condition and the probability after the test results are obtained (also called the posttest probability). Many studies, however, present the properties of diagnostic tests in less clinically useful terms: sensitivity and specificity. Sensitivity denotes the proportion of people with the disorder in whom the test result is positive. Specificity denotes the proportion of people without the disorder in whom the test result is negative. Application of the guides presented in this article can allow surgeons to assess critically studies regarding a diagnostic test.
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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 | 0.100 | 0.580 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".