The Effect of COVID-19 on Length of Stay in Hospital and Patient Population Following Burn Injury
Bibliographic record
Abstract
Acute burn care is heavily resource-dependent and thus was significantly impacted by the COVID-19 pandemic. This study sought to examine the relationship between COVID-19 and the length of stay (LOS) in hospital following burn injury, as prolonged admissions have implications on both individuals and healthcare systems. Additionally, this study explored how COVID-19 affected the homeless burn population, as homelessness has been associated with longer hospital admissions due to limited post-discharge resources. Single-center, retrospective cohort study using data from the Burn Registry and medical chart review with inclusion of all adult burn patients admitted to a quaternary provincial burn unit from April 1, 2016, to March 31, 2023. Patients admitted prior to April 1, 2020, were considered the pre-COVID cohort. Key variables included demographic characteristics and LOS, with homelessness defined as a lack of a fixed address. Of 498 included patients, 301 and 197 were in the pre-COVID and COVID cohorts, respectively. While both cohorts had similar age and gender distributions, a significant difference was noted in LOS between cohorts, with COVID cohort patients staying in hospital for 22 (24) days compared to 20 (29) days in the pre-COVID cohort. More notably, a 58% increase in homeless patients was seen during COVID, with 17% (50/301) of admitted patients being homeless pre-COVID compared to 26% (52/197) during COVID (P < .05). The COVID-19 pandemic resulted in a slightly increased LOS in burn patients, with homeless patients disproportionately affected. This has important implications for both patient outcomes and healthcare resource allocation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".