Real-World Impact of Laparoscopic Surgery for Rectal Cancer: A Population-Based Analysis
Bibliographic record
Abstract
Background: Randomized trials have demonstrated equivalent oncologic outcomes and decreased morbidity in patients with rectal cancer who undergo laparoscopic surgery (LapSx) compared with open surgery (OpenSx). The objective of the present study was to compare short-term outcomes after LapSx and OpenSx in a real-world setting. Methods: A national discharge abstract database was used to identify all patients who underwent rectal cancer resection in Canada (excluding Quebec) from April 2004 through March 2015. Short-term outcomes examined included same-admission mortality and length of stay (los). Results: Of 28,455 patients, 82.4% underwent OpenSx, and 17.6%, LapSx. The use of LapSx increased to 34% in 2014 from 5.9% in 2004 (p < 0.0001). Same-admission mortality was lower among patients undergoing LapSx than among those undergoing OpenSx (1.08% and 1.95% respectively, p < 0.0001). On multivariable analysis, the odds of same-admission mortality with LapSx was 36% lower than that with OpenSx (odds ratio: 0.64; p = 0.003). Median los was shorter after LapSx than after OpenSx (5 days and 8 days respectively, p = 0.0001). The strong association of LapSx with shorter los was maintained on multivariable analysis controlling for patient, surgeon, and hospital factors. Conclusions: For patients with rectal cancer, shorter los and decreased same-admission mortality are associated with the use of LapSx compared with OpenSx.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".