Oncologists of Canada (GOC) and the Canadian Strategy for Cancer Control (CSCC) Clinical Practice Guidelines Action Group (CPG-AG) met to
Bibliographic record
Abstract
determine how GOC would like to influence practice in the care of women with gynecologic cancer. explore a collaborative model for developing and implementing evidence-based practice guidelines. investigate the utility of the CPG evaluation and adaptation cycle as a tool for selecting, adapting, and adopting guidelines. At the workshop meeting, 21 members of the GOC and the CPG-AG heard presentations from various Canadian guideline initiatives. As an example of adaptation and adoption processes, the AGREE (Appraisal of Guidelines for Research and Evaluation) tool was applied to guidelines in recurrent ovarian cancer, and the group explored their opportunity to use knowledge translation to influence the care of women with gynecologic cancer. The themes influencing practice are consistent with GOC’s mandate. The future is expected to involve partnering with other groups to maximize scarce resources. Resources should be directed to facilitating implementation of existing guidelines rather than to developing new documents. The full spectrum of cancer care includes prevention, screening, diagnosis, primary treatment, follow-up, treatment of recurrent disease, and palliation. High-quality evidence is available in some areas, but gaps exist where guideline panels could provide guidance. Development of a pan-Canadian gynecologic oncology process could provide an opportunity to influence access to care at the political and policy levels. The GOC will develop linkages such that the toolbox available through CSCC-CPG-AG can be incorporated into future collaboration.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".