The Optimum Organization for the Delivery of Colposcopy Service in Ontario
Bibliographic record
Abstract
OBJECTIVE: To determine the optimum organization for colposcopy service delivery in Ontario, Canada. METHODS: A multidisciplinary expert panel was convened to develop a systematic review to inform organizational guidelines. MEDLINE, EMBASE, CINAHL, HealthSTAR, and the Cochrane Library databases were searched from 1996 to February 2006 for articles that reported guidance or outcomes relating to improved outcomes in colposcopy training, qualifications, accreditation, maintenance of competency, the delivery of colposcopy, reducing default from colposcopy clinics, and/or strategies to improve patient satisfaction or comfort. In addition, an environmental scan identified unpublished documents related to the delivery of colposcopy services. RESULTS: Sixteen guidance documents related to the delivery of colposcopy services were identified; 5 from the published literature and 11 from the environmental scan. These documents were used by the panel to inform the systematic review and companion guidelines. CONCLUSIONS: Overall, the Ontario Colposcopy Guidelines Development Group believes that the benefits associated with the implementation of colposcopy recommendations in Ontario will result in greater organization of care and improved patient outcomes. In addition, the group anticipates that these recommendations will provide useful guidance to regional planning authorities, hospital administrators, and Cancer Care Ontario, as well as colposcopists and other practitioners, in the planning of integrated regional and provincial cancer screening services.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".