The impact of audit and feedback on nodal harvest in colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Adequate nodal harvest (≥ 12 lymph nodes) in colorectal cancer has been shown to optimize staging and proposed as a quality indicator of colorectal cancer care. An audit within a single health district in Nova Scotia, Canada presented and published in 2002, revealed that adequate nodal harvest occurred in only 22% of patients. The goal of this current study was to identify factors associated with adequate nodal harvest, and specifically to examine the impact of the audit and feedback strategy on nodal harvest. METHODS: This population-based study included all patients undergoing resection for primary colorectal cancer in Nova Scotia, Canada, from 01 January 2001 to 31 December 2005. Linkage of the provincial cancer registry with other databases (hospital discharge, physician claims data, and national census data) provided clinicodemographic, diagnostic, and treatment-event data. Factors associated with adequate nodal harvest were examined using multivariate logistic regression. The specific interaction between year and health district was examined to identify any potential effect of dissemination of the previously-performed audit. RESULTS: Among the 2,322 patients, the median nodal harvest was 8; overall, 719 (31%) had an adequate nodal harvest. On multivariate analysis, audited health district (p < 0.0001), year (p < 0.0001), younger age (p < 0.0001), non-emergent surgery (p < 0.0001), more advanced stage (p = 0.008), and previous cancer history (p = 0.03) were associated with an increased likelihood of an adequate nodal harvest. Interaction between year and audited health district was identified (p = 0.006) such that the increase in adequate nodal harvest over time was significantly greater in the audited health district. CONCLUSIONS: Improvements in colorectal cancer nodal harvest did occur over time. A published audit demonstrating suboptimal nodal harvest appeared to be an effective knowledge translation tool, though more so for the audited health district, suggesting a potentially beneficial effect of audit and feedback strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".