Evidence-based clinical practice guidelines for prostate cancer: the need for a unified approach
Bibliographic record
Abstract
PURPOSE OF REVIEW: Clinical practice guidelines are being increasingly recognized as critically important to an evidence-based practice. This article reviews the different approaches used by leading urological organizations to the development of prostate cancer guidelines. It further introduces the recommendations of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) working group as a unified approach to guideline development. RECENT FINDINGS: Clinical guidelines on the management of prostate cancer demonstrate major methodological differences. Most notably, considerable discrepancies with regards to the systems used to grade the quality of the evidence and the strength of recommendation exist. The GRADE approach classifies the quality of evidence as high, moderate, low or very low, according to factors that include study design and execution, and the consistency of the results. It subsequently classifies recommendations as strong or weak, according to the balance between benefits and downsides and the degree of confidence in estimates of the downsides. SUMMARY: There is an urgent need to standardize processes used to develop clinical guidelines for the management of patients with prostate cancer by leading urological organizations. Adoption of the GRADE approach would offer considerable rewards in terms of efficiency, guideline credibility and optimal clinical decision-making.
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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 | 0.155 | 0.428 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".