KEEPING CANCER GUIDELINES CURRENT: RESULTS OF A COMPREHENSIVE PROSPECTIVE LITERATURE MONITORING STRATEGY FOR TWENTY CLINICAL PRACTICE GUIDELINES
Bibliographic record
Abstract
OBJECTIVES: To describe a methodology used to keep practice guidelines up to date and to summarize data collected during the first year of implementing this plan with a cancer practice guidelines program. METHODS: The updating strategy includes regular searches of peer-reviewed literature and meeting proceedings, review and interpretation of new evidence, review and revision of clinical recommendations, and notification to practitioners and policy makers about new evidence and its impact on recommendations. RESULTS: Eighty pieces of new evidence were found relating to seventeen of the twenty guidelines included in this study. On average, four pieces of new evidence were found per guideline, but there was considerable variation across the guidelines. Of the eighty pieces, nineteen contributed to modifications of clinical recommendations in six practice guidelines, whereas the remaining evidence served to support the original recommendations. None of the modifications led to changes that advised against original recommendations. MEDLINE, the Cochrane Library, and meeting proceedings yielded many pieces of evidence, whereas CancerLit and HealthStar did not contribute significantly to the overall yield. Furthermore, key pieces of evidence that led to modifications to the recommendations were often identified by members of the disease site groups before appearing in electronic databases. CONCLUSIONS: The updating process is resource intensive but yields important findings. In response to this evaluation, the updating protocol has been revised such that literature searches are conducted quarterly and the scope of sources searched routinely is restricted to MEDLINE, the Cochrane Library, and meeting proceedings.
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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 | 0.245 | 0.617 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.057 | 0.058 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".