Improvements are Needed in Reporting of Accuracy Studies for Diagnostic Tests Used for Detection of Finfish Pathogens
Bibliographic record
Abstract
Indices of test accuracy, such as diagnostic sensitivity and specificity, are important considerations in test selection for a defined purpose (e.g., screening or confirmation) and affect the interpretation of test results. Many biomedical journals recommend that authors clearly and transparently report test accuracy studies following the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines ( www.stard-statement.org ). This allows readers to evaluate overall study validity and assess potential bias in diagnostic sensitivity and specificity estimates. The purpose of the present study was to evaluate the reporting quality of studies evaluating test accuracy for finfish diseases using the 25 items in the STARD checklist. Based on a database search, 11 studies that included estimates of diagnostic accuracy were identified for independent evaluation by three reviewers. For each study, STARD checklist items were scored as "yes," "no," or "not applicable." Only 10 of the 25 items were consistently reported in most (≥80%) papers, and reporting of the other items was highly variable (mostly between 30% and 60%). Three items ("number, training, and expertise of readers and testers"; "time interval between index tests and reference standard"; and "handling of indeterminate results, missing data, and outliers of the index tests") were reported in less than 10% of papers. Two items ("time interval between index tests and reference standard" and "adverse effects from testing") were considered minimally relevant to fish health because test samples usually are collected postmortem. Modification of STARD to fit finfish studies should increase use by authors and thereby improve the overall reporting quality regardless of how the study was designed. Furthermore, the use of STARD may lead to the improved design of future studies.
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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 | 0.779 | 0.934 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.040 | 0.034 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".