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Record W2984491366 · doi:10.4314/ahs.v19i3.32

Delayed surgery leads to reduced elbow range of motion in children with supracondylar humeral fractures managed at a referral hospital in sub-Saharan Africa

2019· article· en· W2984491366 on OpenAlexaff
Edward Gakuya Mutheke, Benjamin Mbindyo, Michael Hawkes

Bibliographic record

VenueAfrican Health Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineReferralElbowOrthopedic surgeryRange of motionPhysical therapyCohortProspective cohort studyCohort studySurgeryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Supracondylar humeral fractures (SHFs) in children are associated with morbidity due to elbow stiffness. Timely operative management and/or physiotherapy are thought to reduce this complication, but pose challenges in settings with limited resources for health. METHODS: This prospective cohort study included 45 pediatric patients with isolated SHF at a large tertiary hospital in Nairobi, Kenya. Patients were managed non-operatively or operatively with varying wait times to surgery, with or without physiotherapy. The measurement of elbow ROM was done up to 12 weeks after removal of Kirshner wires and/or backslab. RESULTS: Elbow ROM increased in the follow-up period, yet residual restricted mobility in the flexion-extension plane was common. Delayed surgical management ≥7 days was associated with reduced elbow ROM in the flexion-extension plane at 12 weeks median IQR 105° 92°-118° vs 120° 108°-124°, p=0.029. Physiotherapy was associated with reduced ROM at 12 weeks p=0.003, possibly due to the use of prolonged immobilization. CONCLUSION: In this study of pediatric SHFs at a resource-limited hospital, elbow flexion was restricted at 12 weeks follow-up and was associated with major delays in operative management. Quality of orthopedic surgical care and physiotherapy services in low-resource settings deserves further attention.

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)0.001
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 score0.006
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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