Self-reported practice patterns and knowledge of rectal cancer care among Canadian general surgeons
Bibliographic record
Abstract
BACKGROUND: Our objective was to examine the knowledge and treatment decision practice patterns of Canadian surgeons who treat patients with rectal cancer. METHODS: A mail survey with 6 questions on staging investigations, management of low rectal cancer, lymph node harvest, surgical margins and use of adjuvant therapies was sent to all general surgeons in Canada. Appropriate responses to survey questions were defined a priori. We compared survey responses according to surgeon training (colorectal/surgical oncology v. others) and geographic region (Atlantic, Central, West). RESULTS: The survey was sent to 2143 general surgeons; of the 1312 respondents, 703 treat patients with rectal cancer. Most surgeons responded appropriately to the questions regarding staging investigations (88%) and management of low rectal cancer (88%). Only 55% of surgeons correctly identified the recommended lymph node harvest as 12 or more nodes, 45% identified 5 cm as the recommended distal margin for upper rectal cancer, and 70% appropriately identified which patients should be referred for adjuvant therapy. Surgeons with subspecialty training were significantly more likely to provide correct responses to all of the survey questions than other surgeons. There was limited variation in responses according to geographic region. Subspecialty-trained surgeons and recent graduates were more likely to answer all of the survey questions correctly than other surgeons. CONCLUSION: Initiatives are needed to ensure that all surgeons who treat patients with rectal cancer, regardless of training, maintain a thorough and accurate knowledge of rectal cancer treatment issues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".