Evaluation of the Impact of the Urgent Cancer Care Clinic on Emergency Department Visits, Primary Care Clinician Visits, and Hospitalizations in Winnipeg, Manitoba
Bibliographic record
Abstract
The urgent cancer care (UCC) clinic at CancerCare Manitoba (CCMB) opened in 2013 to provide care to individuals diagnosed with cancer and serious blood disorders experiencing complications from the underlying disorder or its treatment. This study examined the impact of the UCC clinic on other health care utilization in Winnipeg, Manitoba, Canada. An interrupted time series study design was used to compare the rates of emergency department (ED) visits, primary care clinician (PCC) visits, and hospitalizations from 1 January 2010 to 31 December 2015. Rates of ED visits were also stratified by ED location, severity, and cancer type. We found a 6% (95% CI 1.00–1.13, p-value = 0.0389) increase in PCC visits, a 7% (95% CI 0.99–1.15, p-value = 0.0737) increase in hospitalizations, a 4% (95% CI 0.86–1.08, p-value = 0.5053) decrease in the rate of ED visits, and a 3% (95% CI 0.92–1.17, p-value = 0.5778) increase in the rate of ED visits during the UCC clinic hours after the UCC clinic opened. The implementation of the UCC clinic had minimal impact on health care utilization. Future work should examine the impact of the UCC clinic on other aspects of healthcare utilization (e.g., number of tests ordered and time spent waiting in CCMB’s main clinics) and patient quality of life and patient and health care provider experience.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".