Surgical Management of Lower Gastrointestinal Hemorrhage: An Analysis of the ACS NSQIP Database
Bibliographic record
Abstract
Background: Despite advances in diagnostics for lower gastrointestinal bleeding, colorectal resection remains the only option when non-surgical management fails. This study examines a cohort of patients who underwent surgery for this indication to determine the effect of procedure type on postoperative outcomes. Methods: We identified all patients who underwent colorectal resection for bleeding in the ACS NSQIP Participant Use Data File and the Procedure Targeted PUF for colectomy from 2012 to 2013. We compared patients who underwent partial versus total colectomy using univariate analyses and multivariable logistic regression. Results: Of 38,486 colorectal resections performed for bleeding, 85.3% underwent a partial colectomy and 14.7% underwent total colectomy. Patients who had total colectomy were more likely to receive more than four units of blood prior to surgery and have operative times longer than 180 min. Patients who had partial colectomy were more likely to have laparoscopic procedures and to have a stoma created during surgery. On univariate analysis, total colectomy was associated with increased risk of postoperative ileus, cardiac and renal complications, and mortality. On multivariate analysis, total colectomy was associated with increased risk of cardiac and renal complications. Conclusion: The most common procedure performed for lower gastrointestinal hemorrhage was partial colectomy. J Curr Surg. 2017;7(1-2):4-6 doi: https://doi.org/10.14740/jcs307w
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".