Rates and causes of 30-day readmission and emergency room utilization following head and neck surgery
Bibliographic record
Abstract
BACKGROUND: Unplanned returns to hospital are common, costly, and potentially avoidable. We aimed to investigate and characterize reasons for all-cause readmissions to hospital as in-patients (IPs) and visits to the Emergency Department (ED) within 30-days following patient discharge post head and neck surgery (HNS). METHODS: Retrospective case series with chart review. All patients within the Department of Otolaryngology - Head and Neck Surgery who underwent HNS for benign and malignant disease from January 1, 2010 to May 31, 2015 were identified. The electronic medical records of readmitted patients were reviewed for reasons of readmission, demographic data, and comorbidities. RESULTS: Following 1281 surgical cases, there were 41 (3.20%) IP readmissions and 109 (8.43%) ED visits within 30-days after discharge for HNS. For IP readmissions, most common causes included infection (26.8%), respiratory symptoms (17.1%), and pain (17.1%). Most common reasons for ED visits were for pain (31.5%), bleeding (17.6%), and infection (14.8%). Readmitted IPs had significantly higher health burden at pre-operative baseline as compared to patients who visited the ED when assessed with the American Society of Anesthesiology scores (p = 0.002) and the Cumulative Illness Rating Scale (p = 0.004). CONCLUSION: Rate of 30-day IP readmission and ED utilization was 3.20 and 8.43%, respectively. Pain and infection were common causes for returns to hospital. Discharge planning may be improved to target common causes for post-surgical hospital visits in order to decrease readmission rates.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".