Reporting studies on time to diagnosis: proposal of a guideline by an international panel (REST)
Bibliographic record
Abstract
BACKGROUND: Studies on time to diagnosis are an increasing field of clinical research that may help to plan corrective actions and identify inequities in access to healthcare. Specific features of time to diagnosis studies, such as how participants were selected and how time to diagnosis was defined and measured, are poorly reported. The present study aims to derive a reporting guideline for studies on time to diagnosis. METHODS: Each item of a list previously used to evaluate the completeness of reporting of studies on time to diagnosis was independently evaluated by a core panel of international experts (n = 11) for relevance and readability before an open electronic discussion allowed consensus to be reached on a refined list. The list was then submitted with an explanatory document to first, last and/or corresponding authors (n = 98) of published systematic reviews on time to diagnosis (n = 45) for relevance and readability, and finally approved by the core expert panel. RESULTS: The refined reporting guideline consists of a 19-item checklist: six items are about the process of participant selection (with a suggested flowchart), six about the definition and measurement of time to diagnosis, and three about optional analyses of associations between time to diagnosis and participant characteristics and health outcomes. Of 24 responding authors of systematic reviews, more than 21 (≥88 %) rated the items as relevant, and more than 17 (≥70 %) as readable; 19 of 22 (86 %) authors stated that they would potentially use the reporting guideline in the future. CONCLUSIONS: We propose a reporting guideline (REST) that could help authors, reviewers, and editors of time to diagnosis study reports to improve the completeness and the accuracy of their reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.437 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".