Development of prostate cancer quality indicators: a modified Delphi approach.
Bibliographic record
Abstract
OBJECTIVES: There is evidence of variation in both the processes and outcomes of prostate cancer care, resulting in possible harm to patients and increased costs to the health system. Care could be improved by first identifying critical, measurable indicators that correlate with quality of care. This work was conducted to develop indicators of prostate cancer care using a modified three-step Delphi approach. METHODS: A 17-member multidisciplinary panel reviewed potential indicators extracted from the medical literature through two consecutive rounds of rating followed by consensus discussion. The panel then prioritized the indicators selected in the previous two rounds. RESULTS: Of 31 possible indicators that emerged from 49 reviewed articles, 11 were prioritized by the panel as benchmarks for assessing the quality of surgical care for prostate cancer. The 11 indicators represent three levels of measurement (regional, hospital, individual provider) across several phases of care (diagnosis, surgery, pathology, and follow-up), as well as broad measures of outcomes. CONCLUSION: A systematic evidence- and consensus-based approach was used to develop quality indicators of prostate cancer care, with a focus on pre-, peri- and post-operative care as well as outcomes. Some of the indicators selected by the panel were also recommended by a similarly structured panel process. These indicators can be used by individual providers and organizations to monitor the quality of their services, and develop interventions to address any variations.
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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 | 0.156 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".