What are the predictors of emergency department utilization and readmission following extremity bone sarcoma resection?
Bibliographic record
Abstract
INTRODUCTION: Treatment for bone sarcomas are large undertakings. Emergency department (ED) visits and unplanned hospital readmissions are a potential target for cost containment. The purpose of this study was to evaluate the risk factors for ED visits and unplanned readmissions following extremity bone sarcoma surgery. METHODS: Data from Optum Labs Data Warehouse, a national administrative claims database, was analyzed to identify patients with extremity bone sarcomas from 2006 to 2017. Multivariable logistic regression was used to identify factors associated with ED visits and readmissions. RESULTS: Of 1390 (743 males, 647 female) adult patients, 137 (12%) visited the ED and 245 (18%) were readmitted within 30 days of discharge. The most common indication for ED visits (n = 63, 45.9%) and readmission (n = 119, 48.5%) were complications of surgery. Length of stay >10 days was associated with ED utilization (OR, 1.83; P = .01) and readmission (OR, 4.47; P < .001). CONCLUSION: One in ten patients will use the ED, and one in five patients will be readmitted to the hospital within 30 days of discharge following extremity bone sarcoma surgery. Length of stay was associated with ED visits and readmission. These patients could be targeted with alternative management strategies in the outpatient setting with early clinical follow-up to minimize readmission.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".