Increased use of low anterior resection for veterans with rectal cancer
Bibliographic record
Abstract
BACKGROUND: Two surgical procedures with curative intent are available to patients with rectal cancer: lower anterior resection and abdominoperineal resection; however, lower anterior resection may improve quality of life and functional status. AIM: To examine temporal changes in after lower anterior resection and abdominoperineal resection between 1989 and 2000. Potential factors associated with the use of lower anterior resection were evaluated. METHODS: Using national administrative data, we identified patients who received lower anterior resection or abdominoperineal resection. Logistic regression models examined the association between use of lower anterior resection and time period of surgical resection. RESULTS: A total of 5201 rectal cancer patients underwent resection. The use of lower anterior resection increased from 40.0% (1989-91) to 50.1% (1998-2000) paralleled by a corresponding decline in abdominoperineal resection (60.1 to 49.9%; P < 0.001). Patients who received surgery during 1992-94, 1995-97 and 1998-2000 were 6, 7 and 28% more likely to receive lower anterior resection, when compared with 1989-1991 after adjusting for demographic characteristics, co-morbidity and hospital surgical volume. Older age, lower co-morbidity score and lower hospital surgical volume were predictive of lower anterior resection. CONCLUSIONS: An increase in the use of lower anterior resection for rectal cancer was observed over time. This observed increase in use is not confined to high-volume hospitals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".