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Record W4416935394 · doi:10.1177/17531934251395424

Early mobilization for suspected scaphoid injuries in children: a feasibility study

2025· article· en· W4416935394 on OpenAlexaff
Kevin Cheung, Allison K. Baergen, Anne Tsampalieros, Lamia Hayawi, Holly Livock, Patrick Sachsalber, Sasha Carsen, Kevin Smit, Andrew Tice, Braden Gammon

Bibliographic record

VenueJournal of Hand Surgery (European Volume) · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMobilizationUpper limbScaphoid boneWristOrthopedic surgeryLower limb

Abstract

fetched live from OpenAlex

BACKGROUND: The optimal management of children with a suspected scaphoid fracture and normal radiographs is unclear. Traditionally, children have been treated with empiric cast immobilization or with early CT or MRI to avoid missing an occult fracture. An early range of motion (ROM) protocol for these children may be an alternative strategy. METHODS: We conducted a prospective study to test the feasibility and potential benefits of an early ROM protocol for children with a clinically suspected scaphoid fracture but normal radiographs. Participants were evaluated at 6 weeks and, for those with a confirmed fracture on CT, at 26 weeks after injury. RESULTS: Of the 39 children (median age 12.2 years; interquartile range 10.6 to 13.8 years), 15 had a scaphoid fracture confirmed on CT. Participant retention rate was 36 of 39 (92%; 95% CI: 80 to 97%) at 6 weeks, and 12 of 15 (80%; 95% CI: 55 to 93%) at 26 weeks. Participants tolerated the protocol without concerns. There were no instances of non-union, avascular necrosis, or other adverse events. CONCLUSION: An early ROM protocol for children with suspected scaphoid fractures but normal radiographs may reduce overtreatment and dependence on advanced imaging. Further study is feasible and required. LEVEL OF EVIDENCE: III.

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)0.004
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 score0.010
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.279
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

Citations1
Published2025
Admission routes1
Has abstractyes

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