Alberta Rectal Cancer Initiative (ARCI): Implementation of a provincial rectal cancer clinical pathway quality improvement project
Bibliographic record
Abstract
BACKGROUND:A multi-disciplinary evidence-based clinical pathway with discipline-specific goals was created. Following baseline data collection (2010 – 2013), stakeholders from radiology, oncology, surgery, and pathology were engaged to standardize care and inform reporting schemas. Education days with international experts from each discipline were held to reinforce best practice. Synoptic reporting templates were developed for radiology and surgery. METHODOLOGY:Quality indicators determining adherence to best practice and oncologic outcomes from each discipline were collated and reported. Pathology reports were used as quality assurance for surgical technique and MRI staging. The appropriate use of neoadjuvant therapy was correlated with collaborative staging. These measures were then used to provide on-going individualized audit and feedback reports to practitioners through a secure web-based portal; reports contain individual physician data and aggregate provincial data for each indicator to inform and improve practice. RESULTS:Compared to baseline (2013), by 2015 there was a 14% increase in the use of preoperative staging MRI, provincially. Reporting also improved for essential elements on rectal staging MRI, including distance to mesorectal fascia (22 to 81%), extramural venous invasion (17 to 70%), relation to anal sphincter (29 to 78%), and relation to peritoneal reflection (6 to 64%). Surgical technique improved with 91% of rectal specimens graded as ‘complete’ or ‘near-complete’ and a margin positivity of 7% on pathology. Nearly all (94%) pathology reports were completed synoptically, with 90% reporting all mandatory data elements. CONCLUSION:Implementation of a clinical pathway for rectal cancer has improved uptake of best practice across the clinical continuum; this sustainable multifaceted approach includes education, engagement, feedback reporting, and is easily adaptable to other tumour groups. * Indicates faculty mentor
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".