Recommendations for studying the association of the cancer diagnosis to treatment interval with overall survival: a modified Delphi process
Bibliographic record
Abstract
BACKGROUND: The duration from diagnosis to primary treatment initiation (DTI) is an important interval for patients with cancer, as delayed treatment has been found to be associated with heightened recurrence rates and worsened survival. Studying the association between DTI duration and overall survival (OS) is biased and confounded by clinical triaging, heterogeneous definitions, and variation in analytic approaches. OBJECTIVE: To develop consensus-based guidance for conducting studies investigating the association of DTI duration and OS. METHODS: A multidisciplinary panel was recruited to participate in a three-round modified Delphi approach to develop consensus recommendations on the best methodological practices when studying the association between DTI duration and OS. RESULTS: The Delphi panel consisted of 15 experts in the fields of epidemiology, biostatistics, health services research, oncology, and health policy. A list of 24 recommendations with accompanying elaborations was generated including variable definition and measurement, cohort creation, confounder control, pertinent biases, analytic techniques, and balanced interpretation of results. CONCLUSION: Providing valid evidence of the DTI effect on OS requires careful approaches. This paper offers recommendations on how to improve methodological quality. This will ensure that future studies effectively contribute to evidence-informed practice decisions on appropriate waiting times for patients with cancer.
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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 | 0.563 | 0.669 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".