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Record W4378781762 · doi:10.1093/jbcr/irad078

Serial Casting as an Effective Method for Burn Scar Contracture Rehabilitation: A Case Series

2023· article· en· W4378781762 on OpenAlexaffabout
Stephanie Schetzsle, Weber Wei Chiang Lin, Prabhu Purushothaman, Jie Ding, Peter Kwan, Edward E. Tredget

Bibliographic record

VenueJournal of Burn Care & Research · 2023
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsUniversity of AlbertaUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineContractureCastingRehabilitationBurn injuryRange of motionScarsSurgeryPhysical therapyMaterials science

Abstract

fetched live from OpenAlex

Guidelines and protocols for orthoses in burn scar contracture rehabilitation are limited. The current study aims to determine the optimal frequency of casting, potentially facilitating the development of a serial casting protocol. Previous literature supporting casting has low generalizability due to methodology limitations. Seven patients with burn scar contracted joints, who did not respond to traditional therapy, were recruited in this study. Patients were serially casted once, three times, or five times a week. Joint range of motion was maximized with stretching and exercise techniques before every new cast application. Across all patients, active range of motion increased from 65.8 ± 27.8° to 108.1 ± 23.3° with casting; or from 57.8 ± 16.2% to 96.7 ± 2.9% of normal. Similarly, scars improved from 9.5 ± 1.5 to 4.9 ± 1.4 on the Modified Vancouver Scar Scale score. This therapeutic effect was achieved within an average of 8.5 ± 3.7 d and 4.0 ± 2.2 new cast applications. Given the study findings, the procedures outlined could be used to develop a standardized serial casting protocol for burn scar contracture 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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.470
Teacher spread0.415 · 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 designCase report
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

Citations2
Published2023
Admission routes2
Has abstractyes

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