Physicians' attitudes, beliefs and knowledge concerning ovarian cancer.
Bibliographic record
Abstract
OBJECTIVE: To determine physicians' attitudes, beliefs and knowledge concerning surgical care of women with ovarian cancer. METHODS: A survey was created from items generated from the literature, a focus group and individual interviews. The survey was mailed on two occasions to all practicing gynecologists, general surgeons and urologists in Ontario. RESULTS: 701 responses were received (overall response rate: 43.7%); 293 were eligible responses. The responses were analyzed in terms of four determinants of surgical care: knowledge, practice patterns, perceived goals of surgery and barriers to accessing surgical care. These variables would be influenced by the surgeon's specialty, access to an oncologist (medical or gynecologic) at one's facility and distance of one's facility to the nearest cancer center with a gynecologic oncologist. Surgeon's specialty and distance from the cancer center influenced both the intraoperative surgical plan and referral practices. The most important goals of surgery were survival and optimal debulking. The barriers to care included available operating time and surgical beds. CONCLUSION: We have shown that peer influence has reached a ceiling effect in ovarian cancer and novel approaches are required to ensure appropriate referrals, knowledge transfer and provincial resourcing to expert centers to provide optimal surgical care for women with ovarian cancer.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".