Evaluation of a nurse practitioner-led post-discharge transitional care program for patients with liver disease: A retrospective cohort study
Bibliographic record
Abstract
Background: We evaluated an outpatient, nurse practitioner-led transitional care program with respect to its efficacy in reducing unplanned readmission rates for patients with liver disease. Methods: We conducted a retrospective cohort study using data from an academic health system in Toronto, Ontario. The study included all admissions associated with an ICD10 code of R18 (ascites), I85.0 or I98.3 (variceal bleeding), or K70-K77 (diseases of the liver). Patients were selected to receive the transitional care (intervention group) or not (no-intervention group) by discretion of the hepatologists. We used a proportional hazard model to estimate the associations between receiving the intervention and the marginal probability of 30-, 60-, and 90-day readmission in the presence of death as a competing risk. We conducted sensitivity analyses to examine the robustness of our estimates to various sources of bias, including adjusting for propensity of receiving the intervention estimated using a logistic regression model. Results: A total of 803 admissions were included. Receiving transitional care was associated with a reduction in risk of 30-day readmission (HR 0.51; 95% CI 0.30-0.85), 60-day readmission (HR 0.60; 95% CI 0.40-0.91), and 90-day readmission (HR 0.55; 95% CI 0.37-0.83). The negative associations remained statistically significant under the sensitivity analyses, except for the propensity-adjusted estimate for the 60-day outcome. Conclusions: A nurse practitioner-led transitional care program could be effective in reducing the risk of readmission for patients with liver disease. Future studies are needed to standardize the referral process and prospectively measure the effectiveness and financial value of the program.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".