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Record W2889148397 · doi:10.1007/s11751-018-0315-0

Rehabilitation post-distraction osteogenesis for brachymetacarpia: a case report

2018· article· en· W2889148397 on OpenAlexaff
Emily S. Ho, Catharine S. Bradley, Gregory H. Borschel, Simon P. Kelley

Bibliographic record

VenueStrategies in Trauma and Limb Reconstruction · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsDistraction osteogenesisDistractionRehabilitationRange of motionMedicinePhysical therapyMassagePhysical medicine and rehabilitationSplintsPsychologySurgery

Abstract

fetched live from OpenAlex

Distraction osteogenesis for brachymetacarpia has been described in several small case series and single case reports, but the rehabilitation required to optimize outcomes has not been reported. We present a case report describing the hand rehabilitation program of a 13-year-old girl with congenital brachymetacarpia who underwent distraction osteogenesis of the third metacarpal. Intense weekly hand therapy including desensitization, scar massage, range of motion exercises and splinting was essential up to 28 weeks postoperatively to address the progressive changes in the anatomical structures. At final follow-up, she had full active range of motion, no functional deficits in grasp or in-hand manipulation skills and resumed her participation in competitive baton twirling. Patient and family satisfaction with outcome was high. However, better education regarding the progressive symptoms with distraction and daily challenges of wearing an external fixator would have improved the overall experience. With a strong family commitment to rehabilitation and thorough patient education, distraction osteogenesis for brachymetacarpia has the potential to improve functional and aesthetic outcome in the hand. LEVEL OF EVIDENCE: V.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.496

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)0.0000.000

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 head0.008
GPT teacher head0.279
Teacher spread0.270 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

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