The impact of robotic rectal cancer surgery at a Canadian regional cancer centre: a retrospective cohort study
Bibliographic record
Abstract
Background: Although robotic surgery has several advantages over other minimally invasive surgery (MIS) techniques for rectal cancer surgery, the uptake in Canada has been limited owing to a perceived increase in cost and lack of training. The objective of this study was to determine the impact of access to robotic surgery in a Canadian setting. Methods: We conducted a retrospective cohort study involving consecutive adults undergoing surgical resection for rectal cancer between 2017 and 2020. The primary exposure was access to robotic surgery. Outcomes included MIS utilization, short-term outcomes, total cost of care, and quality of surgical resection. We completed univariate and multivariate analyses. Results: We included 171 individuals in this cohort study (85 in the prerobotic period and 86 in the robotic period). The 2 groups had similar baseline characteristics. A higher proportion of individuals underwent successful MIS in the robotic phase (86% v. 46%, p < 0.001). Other benefits included a shorter mean length of hospital stay (5.1 d v. 9.2 d, p < 0.001). The quality of surgical resection was similar between groups. The total cost of care was $16 746 in the robotic period and $18 808 in the prerobotic period (mean difference −$1262, 95% confidence interval −$4308 to $1783; p = 0.4). Conclusion: Access to robotic rectal cancer surgery increased successful completion of MIS and shortened hospital stay, with a similar total cost of care. Robotic rectal cancer surgery can enhance patient outcomes in the Canadian setting.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".