Variability in Reconstructive Procedures Following Rectal Cancer Surgery in the United States
Bibliographic record
Abstract
PURPOSE: We sought to identify variability in surgical care for rectal cancer across the United States. In particular, we hypothesized that in large areas of the country patients are infrequently treated by proctectomy using restorative ("sphincter-sparing") techniques. METHODS: We used all-payer state hospital discharge data from 21 states to determine county level rates of restorative proctectomy vs nonrestorative proctectomy (with colostomy) for rectal cancer. County of residence data were then used to graphically represent variability in surgical care for rectal cancer. RESULTS: From January 2002 through December 2004, 19,912 proctectomies were performed for rectal cancer. Overall, restorative techniques were used in 50.1% of all patients, whereas nonrestorative techniques were used in 49.9%. In approximately one-fourth of the counties surveyed (n = 125; 26%) nonrestorative techniques were used in greater than 60% of proctectomy cases. In the majority of counties (n = 266; 54%,) nonrestorative techniques were used in 41% to 60% of proctectomy cases. Only 20.0% (n = 98) of counties were characterized by rates of nonrestorative proctectomy below 41%. The extremal quotient was 16.9, indicating significant county variability in colostomy formation for rectal cancer surgery. CONCLUSIONS: There is significant geographic variability in the rates of restorative vs nonrestorative proctectomy for rectal cancer in the United States. Large areas of the country report particularly high rates of colostomy formation after proctectomy. An in-depth population-based analysis designed to identify factors contributing to this variability in surgical treatment of rectal cancer is needed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".