A team approach to improving colorectal cancer services using administrative health data
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Canada and accounts for 11.9% of all cancer-related mortality. Fortunately, previous studies have provided evidence of improved outcomes from access to timely and appropriate health services along the disease trajectory in CRC. As a result, the CIHR/CCNS Team in Access to Colorectal Cancer Services in Nova Scotia (Team ACCESS) was created to build colorectal cancer (CRC) research capacity in Nova Scotia (NS) and to study access to and quality of CRC services along the entire continuum of cancer care. OBJECTIVES: The objectives of this paper are to: 1) provide a detailed description of the methodologies employed across the various studies being conducted by Team ACCESS; 2) demonstrate how administrative health data can be used to evaluate access and quality in CRC services; and 3) provide an example of an interdisciplinary team approach to addressing health service delivery issues. METHODS: All patients diagnosed with CRC in NS between 2001 and 2005 were identified through the Nova Scotia Cancer Registry (NSCR) and staged using the Collaborative Stage Data Collection System. Using administrative databases that were linked at the patient level, Team ACCESS created a retrospective longitudinal cohort with comprehensive demographic, clinical, and healthcare utilization data. These data were used to examine access to and quality of CRC services in NS, as well as factors affecting access to and quality of care, at various transition points along the continuum of care. Team ACCESS has also implemented integrated knowledge translation strategies targeting policy- and decision- makers. DISCUSSION: The development of Team ACCESS represents a unique approach to CRC research. We anticipate that the skills, tools, and knowledge generated from our work will also advance the study of other cancer disease sites in NS. Given the increasing prevalence of cancer, and with national and provincial funding agencies promoting collaborative research through increased funding for research team development, the work carried out by Team ACCESS is important in the Canadian context and exemplifies how a team approach is essential to comprehensively addressing issues surrounding not only cancer, but other chronic diseases in Canada.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".