Laparoscopic surgery for colon cancer: a systematic review.
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer is the second leading cause of cancer-related death in western countries. The objective of this systematic review was to show that laparoscopic-assisted colon resection for cancer is not inferior to open colectomy with respect to cancer survival and perioperative outcomes. METHOD: We performed a comprehensive literature review. Inclusion criteria were adults aged over 16 years with a colon resection for documented colon cancer and randomized controlled trials with laparoscopic- assisted or open resections. We excluded studies that did not document colon cancer recurrence in their article. We assessed data extraction and study quality and performed a quantitative data analysis. RESULTS: Six published and 4 unpublished studies fulfilled our inclusion criteria, with a total of 1262 patients. All primary and secondary outcomes showed good homogeneity, except for morbidity, which was described heterogeneously between the studies. There was no disadvantage to laparoscopic colon resection in any of these primary and secondary outcomes, compared with the conventional open technique. CONCLUSION: The results of this study suggest that, although there is no definitive answer, present evidence indicates that laparoscopic colon cancer resection is as safe and efficacious as the conventional open technique.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| 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) | 0.005 | 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".