Evaluation of the Thoracic Surgery Integrated Care Program at the University Health Network
Bibliographic record
Abstract
Introduction: In 2019, the University Health Network (UHN) implemented the Integrated Care (IC) program in the Thoracic Surgery Department. This program aims to facilitate the transition from hospital to community care for patients by providing support including accessible communication with an IC lead. Based on surgical procedure, patients were placed into low, medium, or high care paths. The IC program evaluation investigated risk of readmission and emergency department (ED) visits up to 90 days post-discharge in thoracic surgery patients. Methods and Analysis: This retrospective cohort study used IC cohorts: 1) Original IC patients discharged from June 2019 - Feb 2020, 2) New IC patients discharged from March 2020 – March 2022 3) Combined IC patients discharged from June 2019 – March 2022. These cohorts were compared to historical patients discharged from June 2018 - Feb 2019. Stratified by care path, readmission and ED visit risk were modelled using log-binomial models, adjusting for age, sex, and residence status. Outcomes: The new IC cohort had higher proportions of ED visits and readmissions compared to all other IC cohorts. In the low care path, there was a 15% reduction in readmission risk (RR: 0.85; 95% CI: 0.56, 1.28) in the new IC cohort compared to historical while there was a 20% reduction in readmission risk (RR: 0.81; 95% CI: 0.55, 1.19) in the combined IC cohort compared to historical. Similar trends followed for ED visits. Confidence intervals indicated insufficient evidence to conclude statistically significantly differences between cohorts. Conclusion: When comparing the results with the original IC cohort, there were higher proportions of readmission and ED visits in the new IC cohort. The attenuation of the risk ratio in the new IC cohort compared to the combined IC cohort (RR: 0.85 vs 0.81) suggests the IC program is less effective with new patients.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".