Management of primary rectal cancer by surgeons in Atlantic Canada: results of a regional survey.
Bibliographic record
Abstract
BACKGROUND: We sought to determine the current practice patterns of general surgeons in Atlantic Canada in the management of primary rectal cancer in relation to surgeon-specific variables. METHODS: We sent mail-out surveys to all practising general surgeons (n = 183) in Atlantic Canada to determine screening preferences, preoperative assessment, the use of neoadjuvant and adjuvant therapy, surgical therapy for rectal cancer and surgeon demographics. We analyzed the responses using χ(2) tests. RESULTS: The response rate was 98 (54%) after 2 mail-outs; there were 82 (49%) eligible responses. Surgeons in practice for 21 years or more were more likely than those with fewer than 21 years of practice to order preoperative ultrasonography of the liver and were less likely to order preoperative computed tomography. Endorectal ultrasonography was ordered routinely by 23% of surgeons, whereas 71% of surgeons would order it if time and resources were available. Surgeons who were not certified by the Royal College of Physicians and Surgeons of Canada were significantly more likely than those who were certified to use neoadjuvant therapy in all patients with rectal cancer (43% v. 12%; p = 0.031). Surgeons who performed more than 10 rectal cancer surgeries per year were significantly more likely than those who performed 10 or fewer surgeries per year to use neoadjuvant treatment for T3 tumours (94% v. 61%; p = 0.007). Surgeons with medical or radiation oncology services in their communities were significantly more likely than those without such services to recommend neoadjuvant treatment in T3 rectal tumours and rectal tumours with pathologic lymph nodes. CONCLUSION: We found significant variation in the management of rectal cancer depending on surgeon-specific variables. The implications of these differences on the outcomes of patients with rectal cancer are unknown.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".