Evaluating Surgical Outcomes: A Systematic Comparison of Evidence from Randomized Trials and Observational Studies in Laparoscopic Colorectal Cancer Surgery
Bibliographic record
Abstract
Background: Laparoscopic surgery for colorectal cancer is a novel healthcare technology, for which much research evidence has been published. The objectives of this work were to compare the oncologic outcomes of this technology across different study types, and to define patterns of adoption on the basis of the literature. Methods: A comprehensive systematic review of the literature was conducted using 1) existing systematic reviews, 2) randomized controlled trials (RCTs), and 3) observational studies. Outcomes of interest were overall survival, and total lymph node harvest. Outcomes were compared for congruence. Adoption was evaluated by means of summary expert opinions in the literature. Results: 1) Existing systematic reviews were of low to moderate quality and displayed evidence of overlap and duplication. 2) Laparoscopy was not inferior to open surgery in terms of oncologic outcomes in any study type. 3) Oncologic outcomes from RCTs and observational studies were congruent. 4) Expert opinion in the literature has been supportive of this technology, paralleling the publication of large RCTs. Conclusions: The evaluation of laparoscopic surgery for colorectal cancer in RCTs and observational studies suggests that it is not inferior to open surgery. Adoption of this technology has paralleled RCT evidence.
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 | 0.327 | 0.668 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".