Impact of Reengineered Discharge Toolkit on Patients Undergoing Total Joint Surgeries
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Poorly coordinated care transitions account for nearly one fifth of Medicare hospital readmissions within 30 days postdischarge. The primary aim of this pilot project was to determine the impact of the Reengineered Discharge (RED) Toolkit on patient knowledge for self-management, satisfaction with the discharge process, readiness for discharge, discharge time, and 30-day readmission rate following hip or knee joint replacement or revision surgeries. Staff adherence with the RED Toolkit was also measured.Thirty adult patients received the intervention of the RED Toolkit. Patient knowledge for self-management ranged from 85.2% to 92.6%; satisfaction with the discharge process scores increased from 33% to 59.2%; patient readiness for discharge scores increased from 2% to 64%. Discharge times decreased. On average, patients left the unit 5.67 (±2.52) hours after the written discharge order. The all-cause 30-day readmission rate was reduced to 3.3%. Staff achieved a RED Toolkit adherence rate of 86.8%. Findings provide a basis for developing a coordinated discharge planning process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it