State of the Science Burn Research: Burns in the Elderly
Bibliographic record
Abstract
Marc G. Jeschke MD PhD Herb A. Phelan MD Steven Wolf MD Kathleen Romanowski MD Sarah Rehou MS Alisa Saetamal MD Joan Weber RN John Schulz III MD Crystal New RN BSN Arek Wiktor MD Charles Foster PharmD Lyndsay Deeter MD Kelly Tuohy BSN RN and the Committee on Elderly Burn Care Advances in burn care have led to significant improvements in the outcomes of burn patients except in the elderly: burn patients ≥65 years of age.1,2 This is reflected in the LD50 for elderly burn patients, which has not significantly changed over the last three decades and is around 30 to 35% TBSA burn.4,8 The lack of improvements is even more impactful when considering that elderly represent the fastest growing population, indicating the expected substantial increase in elderly burn patients over the next decades. Additionally, the amount of burn patients in elderly will not only grow due to the growing population of elderly but also have much higher incidence as elderly are at an increased risk for burn injuries due to thinning skin, decreased sensation, mental alterations, pre-existing comorbidities, and numerous other contributing factors.1–6 The high risk of suffering from burns in the elderly population with the rapid growth of this population will require change to the burn treatment paradigm but, at this time, burn care providers lack treatment guidelines or protocols tailored to the special needs of the elderly burn patient. Complicating elderly burn care is the lack of knowledge about maintaining quality of life, independence, and acceptable long-term outcomes.9,10
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".