The basis for monitoring strategies in clinical guidelines: a case study of prostate-specific antigen for monitoring in prostate cancer
Bibliographic record
Abstract
BACKGROUND: The volume of published literature on the evaluation and use of tests for monitoring purposes is sparse. Our aim was to determine the extent to which recommendations for monitoring prostate-specific antigen to detect recurrent prostate cancer consider key factors that should inform rule-based strategies for monitoring. METHODS: We reviewed the recommendations made in clinical guidelines for the repeated measurement of prostate-specific antigen in men who have received primary treatment for localized prostate cancer. We assessed the guidelines using the Appraisal of Guidelines for Research and Evaluation Framework. RESULTS: We identified guidelines and statements of best practice from nine organizations. We saw considerable inconsistency in recommendations for testing for prostate-specific antigen as a form of monitoring. Recommendations on when to test appeared to be almost exclusively determined using standard follow-up schedules rather than any scientific basis. Recommendations on when to take action were primarily based on consensus statements or retrospective case series. Eight of the nine guidelines acknowledged the potential presence of measurement variability, but they did not attempt to account for the effect of such variability on the interpretation of the results of tests for prostate-specific antigen. Many recommendations were made with few or no supporting references; however, a variety of papers were cited across guidelines. Of 48 papers cited, 29.1% (14/48) were reviews; the remaining 70.8% (34/48) of papers cited were primary studies. INTERPRETATION: A systematic approach to the development of monitoring schedules using prostate-specific antigen in guidelines for prostate cancer is lacking, due to inadequacies in the available evidence and its use.
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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 | 0.216 | 0.605 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| 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 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".