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Record W4210344205 · doi:10.1016/j.burnso.2022.01.002

Wound healing in older adults with severe burns: Clinical treatment considerations and challenges

2022· article· en· W4210344205 on OpenAlexfundno aff
Kathleen S Romanowski, Soman Sen

Bibliographic record

VenueBurns Open · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthMallinckrodt Pharmaceuticals
KeywordsMedicinePopulationBurn woundBurn injuryIntensive care medicineWound healingPopulation ageingSurgery

Abstract

fetched live from OpenAlex

Background: The older adult population continues to rapidly expand in number, with a projection by the United States (US) Census Bureau that there will be more individuals older than > 65 years (77.0 million) than those younger than < 18 years (76.5 million) by 2034. This review provides an overview of aging as it relates to wound healing and burn injuries in older adult patients, summarizes current treatment practices, and addresses the key challenges and considerations for treating severe burn injuries in this specific patient population. Materials and methods: A narrative literature search was conducted, focusing on recent primary literature on burns and wound healing in elderly patients. Results: Studies showed that the aging process results in both physiologic (eg, nutritional and metabolic status) and anatomic changes (eg, thinning dermis) that contribute to a reduced capacity to recover from burn-injury trauma compared with younger patients. Owing to impaired vision, decreased coordination, comorbidities, and medication-induced side effects, older adults (ie, > 65 years) are susceptible to severe burn injury (deep-partial thickness and full-thickness), which is associated with significant morbidity and mortality. Conclusion: A better understanding of the effects of age-related changes regarding wound healing in older adult patients who incur severe burn injuries may provide insight into clinical strategies to improve outcomes among this population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.101
GPT teacher head0.368
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2022
Admission routes1
Has abstractyes

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