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Record W2946068096 · doi:10.1093/jbcr/irz079

The Impact of Introducing a Physical Medicine and Rehabilitation Consultation Service to an Academic Burn Center

2019· article· en· W2946068096 on OpenAlexaff
Lawrence R. Robinson, Matthew Godleski, Sarah Rehou, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2019
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRehabilitationAcute careFunctional Independence MeasurePhysical therapyRetrospective cohort studyBurn centerProspective cohort studyAcute hospitalEmergency medicineCohort studyPoison controlHealth careSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Prior retrospective studies suggest that physical medicine and rehabilitation (PM&R) acute care consultation improves outcome and reduces acute care length of stay (ACLOS) in trauma patients. There have not been prospective studies to evaluate this impact in burn patients. This cohort study compared outcomes before and after the introduction of a PM&R consultation service to the acute burn program, and the inpatient rehabilitation program, at a large academic hospital. The primary outcome measures were length of stay (LOS) in acute care and during subsequent inpatient rehabilitation. For the acute care phase, there were 194 patients in the preconsultation group and 114 who received a consultation. There was no difference in age, Baux score, or LOS in these patients. For the rehabilitation phase, there were 109 patients in the prephysiatrist group and 104 who received PM&R care. The LOS was significantly shorter in the latter group (24 days vs 30 days, P = .002). Functional independence measure (FIM) change, unexpected readmission, and discharge destination were not significantly different. The addition of a burn physiatrist did not influence ACLOS. However, there was a significant reduction in inpatient rehabilitation LOS.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.333
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.458
Teacher spread0.411 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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