Assessing outcomes following surgery for colorectal cancer using quality of care indicators.
Bibliographic record
Abstract
BACKGROUND: We sought to assess the feasibility of applying Cancer Care Ontario's quality of care indicators to a single institution's colorectal cancer (CRC) database. We also sought to assess their utility in identifying areas that require improvement. METHODS: We included patients who had surgery for CRC between 1997 and 2006 at Mount Sinai Hospital, Toronto, Ont. We excluded patients who had transanal excisions, carcinoma in situ or recurrences that required pelvic exenteration, as well as those whose information was incomplete. We obtained data from a prospective database and verified the data with hospital and office charts. We evaluated trends over a 10-year period using the Cochran-Armitage trend test. RESULTS: During the study period there were 1005 surgical procedures performed in 987 patients with a mean age of 65.6 (standard deviation 15) years; the male:female ratio was 1:2. The most frequent tumour sites were the rectum and sigmoid colon (68%). Over the 10-year period, 9 indicators improved, including the proportion of patients with CRC identified by screening (p < 0.001), the proportion of patients who received preoperative liver imaging (p = 0.05), the proportion of rectal cancer patients who received preoperative pelvic imaging (p = 0.04), the proportion of patients with stage II or III rectal cancer who received radiotherapy (p = 0.03), the proportion of surgical specimens with more than 12 lymph nodes (p < 0.001), the proportion of pathology reports that included quantitative distal (p = 0.004) and radial (p < 0.001) margin measurements, the proportion of patients with an anastomotic leak (p = 0.03), the proportion of patients who received a colonoscopy 1 year after surgery (p < 0.001) and the proportion of operative reports that were complete (p < 0.001). CONCLUSION: The use of quality of care indicators to assess the quality of colorectal surgery is feasible. This study provides benchmarks that can be used to assess changes in the quality of CRC care at our institution.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".