Trends in colon cancer surgery in Ontario: 2002–2009
Bibliographic record
Abstract
AIM: The safety and efficacy of laparoscopic surgery for colon cancer is well established but its uptake in the province has not been previously explored. We report an investigation of the trends of open and laparoscopic surgery for colon cancer in Ontario, Canada. METHOD: A retrospective cross-sectional time-series analysis examining population-based rates of elective surgery for colon cancer among 10.5 million adults in Ontario was conducted from 1 April 2002 to 31 March 2009. Databases were linked to assess quarterly elective procedure rates over time. RESULTS: During the study period, 3950 laparoscopic and 13 048 open elective colon cancer operations were performed in Ontario. The overall quarterly rate of colon cancer surgery remained stable at an average of 5.8 per 100000 population (P=0.10). From the first and last quarter, the rate of laparoscopic operations increased nearly threefold from 0.8 to 2.2 per 100000 population with a notable increase after 2005 (P<0.01). In contrast, open surgery decreased by more than 30% from 5.3 to 3.5 per 100 000 population (P<0.01). If current trends continue, the projected proportion of laparoscopic colon operations is estimated to reach 41% by 2015. Patients receiving open surgery had a significantly higher preoperative comorbidity (Charlson comorbidity score≥3) than those having laparoscopy (47.8%vs 39.1%, standardized difference 0.26). CONCLUSION: Trends in Ontario of laparoscopic colon cancer surgery show an increase between 2002 and 2009, but the incidence remains lower than for open surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".