Improving prostate cancer care collaboratively - a multidisciplinary, formal, consensus-based approach.
Bibliographic record
Abstract
INTRODUCTION: There are numerous standard treatment options for men diagnosed with localized prostate cancer. Multidisciplinary consultation before decision-making is a consensus- and quality-based objective in Ontario. With the goals of working together more collaboratively and to provide higher quality information for patients at the time of decision-making, a prostate cancer community partnership consensus (PCPC) panel was formed among six partnering centers in the Greater Toronto Area. MATERIALS AND METHODS: Five iterative meetings were held among 40 prostate cancer specialists (32 urologists and 8 radiation oncologists) who participate in multidisciplinary clinics. The meetings defined the goals of the partnership as well as the topics and questions the group would address together. Answers to these questions were developed by formal consensus: >= 75% of participants had to agree with wording based on secret ballots to achieve consensus. RESULTS: All six groups wanted to participate to improve patient care/decision-making. Forty-one questions addressing 30 issues were derived from the literature and the group's collective experience. These issues were cross-tabbed against five management options: active surveillance, radical prostatectomy, low dose rate brachytherapy, high dose rate brachytherapy boost and external beam radiation. Answers common to all modalities were coalesced. Eighty-six issues were subjected to formal consensus. After three rounds of secret ballots, consensus was achieved for the answers to all issues. CONCLUSIONS: A formal consensus-based partnership between urology and radiation oncology to support newly diagnosed prostate cancer patients was feasible and resulted in a patient information guide which may improve 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 | 0.162 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.006 | 0.011 |
| 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".