625 The Impact of COVID-19 on Burn Care at a Regional Burn Center
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic has had a profound global impact, not least on hospital functioning. Institutions have all had to prepare and adapt to a large number of admissions, and the influence on elective and emergency surgical services, including burn care, has been significant; it may be some time before we know the full extent of this. While many centers were able to commence more normal activities for a while, we are now seeing an exponential rise in cases again, with potentially catastrophic consequences for the provision of burn care. Methods A review of all admissions, operative cases and clinic visits between 1 April and 31 August 2020 was undertaken at an American Burn Association verified burn center. These data were compared with the same five-month period in the preceding two years. Results Selected data highlights are tabulated (Table 1). During the five months in question, fewer patients were admitted than the previous two years (N=81 versus 121). The mean total body surface area was slightly higher this year (13.7%), and the mean length of hospital stay longer (18 days). The male-to-female ratio of admitted patients was greater during the five months of 2020, at 2.9:1, compared to 1.7:1. No significant differences in terms of etiology were detected, however. As expected, clinic visits reduced dramatically from a mean of 160 patient visits per month to just 81 per month, with the majority conducted virtually. During 2020 the operative cases were similar in number to previous years (N=176), but the mean duration was significantly longer (190 minutes). The total time utilised for burn surgery was similar to previous years (572 hours). Table 1. Selected burn center data comparing 2020 with 2019 and 2018. Conclusions This study demonstrates that although total admissions were slightly reduced, the demands on Burn ICU bed resources and burn operating time were similar. The data supports the notion that removing scheduled operating time for our service resulted in less efficient execution of acute burn surgeries and longer hospital stays. Although formal clinic visits were significantly reduced and were mainly conducted virtually, several patients were satisfied by a novel and user-friendly email service conducted by our clinic nurse specialist.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".