Determinants of Home Discharge Among Survivors of Necrotizing Fasciitis: A Population-Level Analysis
Bibliographic record
Abstract
BACKGROUND: The majority of patients with necrotizing fasciitis (NF) in the United Sates survive their illness, and there is increasing interest in addressing the ability of survivors to return to their community following hospitalization. However, there are no data on the factors affecting home discharge among survivors of NF. METHODS: We used the Texas Inpatient Public Use Data File to conduct a retrospective population-based examination of hospitalizations with NF aged 15 years or older between 2001 and 2010. Multivariate logistic regression modeling was used to explore predictors of home discharge among hospital survivors. RESULTS: There were 10,724 NF hospitalizations surviving to discharge during study period, of which 62.5% were discharged home. The following key predictors have adversely affected odds of home discharge (odds ratio and 95% confidence intervals): age ≥ 75 years (0.349 (0.292 - 0.417)), Medicare insurance (0.582 (0.510 - 0.663)), congestive heart failure (0.836 (0.719 - 0.972)), chronic liver disease (0.684 (0.522 - 0.895)), respiratory failure (0.464 (0.386 - 0.558)), neurological failure (0.573 (0.418 - 0.787)), and need for mechanical ventilation (0.339 (0.199 - 0.578)). Increased odds of home discharge were found among males (1.116 (1.058 - 1.285)), Hispanics (1.193 (1.056 - 1.349)), those lacking health insurance (2.161 (1.183 - 2.521)) or managed at a teaching hospital (1.264 (1.127 - 1.418)). CONCLUSIONS: In this first population-level examination of the determinants of home discharge among survivors of NF, older age, Medicare insurance, selected comorbidities, and development of organ failure decreased patients' odds of home discharge. Unexpectedly, male gender, Hispanic ethnicity, lack of health insurance, and being managed at a teaching hospital were associated with favorable impact on patients' discharge disposition. Further studies are warranted in other populations and healthcare environments to corroborate the present findings and to refine our understanding of the factors affecting short-term outcomes among NF survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".