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Record W2016001674 · doi:10.1097/bcr.0b013e318198a2a7

A 12-Year Comparison of Common Therapeutic Interventions in the Burn Unit

2009· article· en· W2016001674 on OpenAlexaboutno aff
Christopher E. Whitehead, M. Serghiou

Bibliographic record

VenueJournal of Burn Care & Research · 2009
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionRehabilitationUnit (ring theory)Family medicineNursingPhysical therapy

Abstract

fetched live from OpenAlex

Although most occupational and physical therapists in an acute burn care setting use similar therapy practices, the time frames at which these therapeutic interventions are carried out vary according to the burn centers' practices. The purpose of this survey was to investigate current trends in burn rehabilitation and compare the results with a similar survey performed in 1994. The survey was designed in a similar fashion to the 1994 survey to ascertain common trends in burn rehabilitation. The survey was sent to 100 randomly selected burn care facilities throughout the United States and Canada. Content included rehabilitation interventions, including evaluation, positioning, splinting, active range of motion, passive range of motion, ambulation, as well as the cross-training of therapists. Significant increases in the percentages of burn centers initiating common therapy practices were found. Positioning (41% increase), active range of motion (48% increase), passive range of motion (52% increase), and ambulation (29% increase) were all found to have increases in the number of burn centers employing these practices in the same time frame. Overall comparison from 1994 to 2006 shows that common therapy techniques are being initiated earlier in the patient's acute burn stay. These results are consistent with recent medical trends of earlier acute discharges and more focus on outpatient rehabilitation.

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)0.000
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 score0.175
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.240
GPT teacher head0.505
Teacher spread0.265 · 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

Citations29
Published2009
Admission routes1
Has abstractyes

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