295 Burn Patients that Exceed the Average Length of Stay
Bibliographic record
Abstract
Burn patients show a high variability and poor predictability in their length-of-stay (LOS) due to the complexity of burn injury itself and various complications. To focus care expectation and prognosis we aimed to identify key factors that contribute to prolonged LOS. A retrospective cohort-study was conducted in an adult burn-center between January 2006 and December 2016. We excluded patients that died during hospitalization and patients with <10% TBSA burn. Patients were then stratified into expected-LOS (<2days LOS/%TBSA) and longer-than-expected-LOS (>2days LOS/%TBSA). We assessed demographics, burn etiology, comorbidities, and in-hospital complications. Logistic regression and propensity matching (adjustment/matching for age, gender, inhalation injury, and TBSA% of 3rd degree burns) was utilized. There were 583 patients that met inclusion criteria; and of those, 477 showed an expected-LOS whereas 106 exceeded that time frame. Patients who exceeded their LOS were significantly older, had greater 3rd degree TBSA% burn, and a larger proportion of inhalation injuries (p<0.05). Additionally, there was a significantly greater proportion of these patients that had in-hospital complications of infection, sepsis, and organ failure (p<0.05). Interesting, exceeding LOS patients also had a higher number of pre-existing psychiatric conditions such as depression or schizophrenia. In-hospital complications have a high influence on exceeding the average LOS in burn patients. Burn patients also have a unique and complex set of pre-existing medical conditions such as mental health issues that further complicate their treatment and interferes with an early recovery and discharge. More studies are need to investigate how to modify these critical factors. Progress has been made to update the 1day/%TBSA convention to better aid health care providers in giving appropriate outcomes for patients and their families and to supply intensive care units with valuable data to assess the quality of their care and improve patient prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".