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Record W4409023656 · doi:10.1093/jbcr/iraf019.202

573 Feasibility of an Unfunded Multicenter Trial Group for Older Adult Burn Patients: A Retrospective Study

2025· article· en· W4409023656 on OpenAlexaff
Lauren Nosanov, Alisa Savetamal, Jessica Reynolds, Dhaval Bhavsar, Sam Miotke, Mark Johnston, Lucy Wibbenmeyer, Colette Galet, David Hill, Sara Higginson, Theresa L. Chin, Andrea Long, Emily Baker, Alexandra Lacey, Rosemary Paine, Diana Julia Tedesco, Shawn Tejiram, Marc G. Jeschke, Kathleen S Romanowski

Bibliographic record

VenueJournal of Burn Care & Research · 2025
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineRetrospective cohort studyMulticenter studyEmergency medicineIntensive care medicineSurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

Abstract Introduction Older adults represent a significant portion of the burn patient population, presenting unique physiological challenges and requiring tailored treatment approaches. Yet, comprehensive data specific to this demographic are limited. The relatively small number of older burn patients at each center makes single-center studies insufficient to address key questions about their care. The development of a multicenter trial group focused on older adult burn patients could yield valuable insights into clinical outcomes, yet funding for such initiatives is often restricted. This study explores the feasibility of creating an unfunded multicenter trial group and conducting a retrospective pilot study to examine clinical trends and outcomes for older adult burn patients. Methods Following IRB approval and the creation of data use agreements, a retrospective pilot study was conducted across twelve burn centers in North America. Each center was tasked with collecting standardized data on burn patients aged 60 and older from January 2017 to December 2019. Data included demographic information, burn characteristics (total body surface area burned, type of burn, etc.), treatment interventions, and outcomes such as mortality, length of hospital stay, and complications. Where possible, data were sourced from each center’s submission to the Burn Care Quality Platform (BCQP). BCQP and additional data were manually entered into a centralized REDCap database managed by one institution. Descriptive statistics of the pilot study are presented. Results The study included 1,632 older adult burn patients. Median age was 68 years (interquartile range [IQR] 13); 1,095 patients (67%) were male and 1,187 (73%) were White with a median BMI of 27.5 (IQR 7.87). The median burn size was 3.5% total body surface area (IQR 9, range 0-100%). Patients presented a median of 4.68 hours after injury (IQR 15.35), with a median modified Baux score of 76.21 (IQR 20.5). These patients underwent a median of 1 operation (IQR 1), typically 3 days after injury (IQR 6). The median length of hospital stay was 6 days (IQR 13), with an ICU stay of 1 day (IQR 5). In-hospital mortality was 10.4% and the median time to wound healing was 40 days (IQR 48). Conclusions Creating an unfunded multicenter trial group for older adult burn patients is feasible. The group successfully conducted a retrospective study on care trends for burn-injured older adults. Key factors in the success of this initiative included standardized data collection protocols and strong collaboration between centers. Applicability of Research to Practice Although challenges remain, this project illustrates the potential for establishing a larger, sustainable multicenter trial group aimed at improving clinical practices and outcomes for older burn patients. Funding for the Study No financial compensation or technical support was provided to the participating institutions.

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 imitation

Not 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.

metaresearch head score (Codex)0.120
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.120
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.129
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.077
GPT teacher head0.468
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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