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Record W4413044663 · doi:10.1186/s12913-025-13049-1

Evaluation of an integrated care program for thoracic surgery in Ontario, Canada: a historical cohort study

2025· article· en· W4413044663 on OpenAlexaffabout
Nicholas Bakewell, Catherine Y. Liang, Tsoleen Ayanian, Sanjana Sundaram, Kazuhiro Yasufuku, Meghan O’Neill, Kathy Kornas, Lori Diemert, Megan S. Lowe, Melissa Chang, Laura C. Rosella

Bibliographic record

VenueBMC Health Services Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity Health NetworkUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsMedicineNursing researchHealth administrationHealth informaticsPublic healthCardiothoracic surgeryCohortHealth services researchHealth careCohort studyGeneral surgerySurgeryFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Integrated care (IC) may help to improve postoperative health outcomes among thoracic surgery patients. We conducted an outcome evaluation of an IC program implemented within the Division of Thoracic Surgery of a large hospital network in Ontario, Canada. METHODS: Historical cohort design using data on patients who underwent thoracic surgery to compare outcomes of two IC groups (Pre-COVID IC: June 2019-February 2020; COVID IC: March 2020-September 2022), to a Historical non-IC group (June 2018-February 2019). Stratified by care path (low [minimally invasive procedures], medium [other procedures], high [complex procedures]), we compared risks of readmissions and emergency department (ED) visits, and mean length of stay (LOS) and healthcare costs up to 90 days post-discharge using modified Poisson/Ordinary Least Squares, Negative Binomial and Gamma regression, respectively, adjusting for age, sex, and location of residence. RESULTS: In total, 1572 patients were included (Pre-COVID IC (n = 269); COVID IC (n = 869); Historical non-IC (n = 434)), with the largest proportion enrolled in the low care path. Compared to the Historical non-IC group, both the Pre-COVID and COVID IC groups had a statistically significantly shorter mean index LOS in the low (relative mean difference (RMD): 0.74 (95% confidence interval: 0.63–0.88) [Pre-COVID]; 0.66 (0.58–0.75) [COVID]) and medium (RMD: 0.75 (0.59–0.97) [Pre-COVID]; 0.62 (0.51–0.74) [COVID]) care paths; results were similar for total LOS (including readmissions). In contrast, the Pre-COVID IC group had a statistically significantly higher mean index LOS in the high care path (RMD: 1.39 (1.02–1.89)). The Pre-COVID IC group had a statistically significantly lower risk of 90-day ED visits in the low care path (relative risk (RR): 0.59 (0.36–0.97)) and a statistically significantly lower mean index cost in the medium care path (RMD: 0.72 (0.52–0.99)). The COVID IC group had a statistically significantly lower risk of 90-day readmissions in the low care path (RR: 0.64 (0.42–0.97)). CONCLUSIONS: IC programs may reduce post-discharge ED visits, LOS and healthcare costs for thoracic surgery patients, particularly for those in a low care path. As the IC program continues, further research is needed with larger sample sizes to confirm these findings and optimize IC program delivery, especially for more complex surgical patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.225
GPT teacher head0.587
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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