Assessment of quality-of-care indicators for colorectal cancer surgery at a single centre in a developing country
Bibliographic record
Abstract
Background: The implementation of quality-of-care indicators aiming to improve colorectal cancer (CRC) outcomes has been previously described by Cancer Care Ontario. The aim of this study was to assess the quality-of-care indicators in CRC at a referral centre in a developing country and to determine whether improvement occurred over time. Methods: We performed a retrospective study of our prospectively collected database of patients after CRC surgery from 2001 to 2016. We excluded patients who underwent local transanal excision, pelvic exenteration or palliative procedures. We evaluated trends over time using the Cochran–Armitage test for trend. Results: A total of 343 patients underwent surgical resection of CRC over the study period. There was improvement of the following indicators over time: the proportion of patients detected by screening (p = 0.03), the proportion of patients with preoperative liver imaging (p = 0.001), the proportion of patients with stage II or III rectal cancer who received neoadjuvant chemotherapy (p = 0.03), the proportion of patients with pathology reports that indicated the number of lymph nodes examined and the number of positive nodes (p = 0.001), and the proportion of patients with pathology reports describing the details on margin status (p = 0.001). Conclusion: This study showed the feasibility of applying the Cancer Care Ontario indicators for evaluating outcomes in CRC treatment at a single centre in a developing country. Although there was an improvement of some of the quality-of-care indicators over time, policies and interventions must be implemented to improve the fulfillment of all indicators.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".