The investigation of primary rectal cancer by surgeons: current pattern of practice.
Bibliographic record
Abstract
OBJECTIVE: Selection of the optimal treatment strategy for patients with rectal cancer requires appropriate investigation, but published guidelines provide no clear consensus. We examined the current practice pattern for the investigation of primary rectal cancer by general surgeons in the province of Ontario, Canada. METHODS: A telephone interview was completed by 124 surgeons in Ontario who manage patients with rectal cancer, who indicated the investigations they routinely perform in assessment before treatment. An exploratory cluster analysis was used to identify surgeon-related variables that predicted the pattern of investigation; these were tested in univariate and multivariate analyses. RESULTS: Cluster analysis identified 3 distinct groups of surgeons based on patterns of test usage. Univariate analysis showed that the use of chest radiography, computed tomography of the abdomen and pelvis, and ultrasound of the abdomen varied significantly with the surgeon's subspecialty training, practice location and years in practice. Regression analysis confirmed that each of these 3 variables independently predicted the pattern of preoperative investigation. There were no significant predictors of the use of colonoscopy, sigmoidoscopy or carcinoembryonic antigen level. Over half of surgeons reported that they would have ordered additional imaging tests but did not because of lack of availability. The perceived desirability of additional tests, endorectal ultrasound in particular, varied with training. CONCLUSIONS: The current practice pattern for the preoperative investigation of primary rectal cancer by general surgeons varies significantly with specific surgeon-related variables, with potential impact on the management and outcome of patients with rectal 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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".