Presence of Specialty Surgeons Reduces the Likelihood of Colostomy After Proctectomy for Rectal Cancer
Bibliographic record
Abstract
PURPOSE: Geographic variability in the use of restorative proctectomy for rectal cancer has been described throughout the United States. We examined factors associated with high rates of colostomy formation after proctectomy for rectal cancer across US counties. METHODS: We used state hospital discharge data from 21 states to determine county rates of restorative proctectomy vs nonrestorative proctectomy (ie, colostomy) for rectal cancer. We merged the county-level data with 1) tumor characteristics from Surveillance Epidemiology and End Results data; 2) number of specialty surgeons in the American Society of Colon and Rectal Surgeons and Society of Surgical Oncology; 3) county socioeconomic variables from census data; 4) colorectal cancer-screening rates from Medicare; and 5) hospital characteristics from the American Hospital Association. We then determined factors associated with high rates of colostomy formation (> 60%) after proctectomy for rectal cancer across counties. RESULTS: From January 1, 2002, to December 31, 2004, a total of 19,912 proctectomies were performed for cancer in 1050 counties, of which 489 had adequate sample size for evaluation. Based on county of residence information, nonrestorative proctectomy with colostomy was performed in greater than 60% of all patients with rectal cancer in 26% (n = 125) of counties. On multivariate analysis, more specialty surgeons (OR = 0.70; CI = 0.51-0.96) were protective against colostomy formation at the county level. CONCLUSIONS: The use of restorative techniques in rectal cancer surgery varies based on access to specialty colorectal cancer surgeons. Population-based directives are needed to standardize care for rectal cancer across the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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) | 0.002 | 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".