The Impact of an anticipated discharge date communicated by a physician assistant on length of hospital stay after major head and neck surgery : Anticipated discharge date communication by a PA in HEENT Surgery
Bibliographic record
Abstract
Patients undergoing major head and neck (H&N) surgery require complex multidisciplinary care. Surgical ward rooms contain whiteboards primarily used by nurses. The display of an “anticipated discharge date” (ADD) may be a simple yet effective discharge tool. Adults following H&N resection with reconstruction were randomized into two groups: without an ADD (control) or with an ADD (intervention). For the intervention group, the physician assistant (PA) provided an ADD 48-hours prior to and documented it on the whiteboard. The PA also provided additional discharge education. On the day of discharge, all patients completed a survey examining readiness for discharge. Nurses and allied health also completed a survey examining protocol effectiveness. The primary outcome was the length of hospital stay (LOS). Thirty-two patients were examined. There were no statistical differences in demographics, postoperative complications, and days to tracheostomy decannulation. Median LOS was 0.5 days shorter for the intervention group (11.50 vs. 12.00, p=0.84). No patients were readmitted within 30-days and there were no mortalities. There were trends for the intervention group to better understand their hospital course and believe their discharge date was adequately communicated (p=0.18 and p=0.16). Sixty-seven percent of staff believed the ADD assisted their practice, while 83% believed the PA improved efficiency of the discharge process. Surprisingly, providing patients an ADD did not significantly reduce LOS. Despite most patients having advanced cancer and considerable comorbidities, the 30-day readmission rate was zero. The PA improved patient education, while 66% of staff agreed on an ADD positively impacts patient care.
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 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".