Using technology to create a medication safety net for cardiac surgery patients: a nurse-led randomized control trial.
Bibliographic record
Abstract
PURPOSE: Interactive voice response (IVR) technology was used to increase medication compliance and reduce adverse events (hospitalization and emergency visits) in post-cardiac surgery patients. METHOD: Patients randomized to intervention received 11 automated IVR calls in the six months after discharge. A total of 331 patients (164 IVR, 167 usual care) participated. RESULTS: Findings showed significant differences in the IVR group for the primary composite outcome of compliance and adverse events (relative risk (RR] and 95% confidence interval [CI]: 0.60 [0.37, 0.96), p = 0.041) and the secondary outcome of medication compliance (RR: 0.34 (0.20, 0.56), p < 0.0001). There was no significant impact on emergency room visits (RR: 1.04 (0.63, 1.73J) and hospitalization (RR: 0.77 [0.41, 1.45]). Most patients (93%) preferred IVR follow-up to no follow-up.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".