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Record W2948353542 · doi:10.1177/1558944719850635

Pediatric Hand Injuries Requiring Closed Reduction at a Tertiary Pediatric Care Center

2019· article· en· W2948353542 on OpenAlexaff
Marisa Market, Maala Bhatt, Amisha Agarwal, Kevin Cheung

Bibliographic record

VenueHand · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineReduction (mathematics)Retrospective cohort studyTrauma centerPediatric traumaPhalanxSurgeryJoint dislocationMetacarpophalangeal jointEmergency departmentPoison controlInjury preventionThumbEmergency medicine

Abstract

fetched live from OpenAlex

Background: Hand fractures and dislocations are common injuries in the pediatric population. This study aims to characterize the pediatric hand injuries that required closed reduction and identify those that required multiple reduction attempts. Methods: A retrospective cohort study was carried out in patients younger than 18 years of age with hand fractures or dislocations who underwent closed reduction in the emergency department (ED). Patients who ultimately required surgical reduction and fixation were not included. Results: Of the 310 hand injuries identified, 148 (114 fractures and 34 dislocations) underwent closed reduction in the ED; 7.4% of those required repeat reduction. Hand injuries that most often required repeat reduction included metacarpophalangeal joint dislocations (20.0%) and proximal phalanx neck (16.7%), metacarpal shaft (15.4%), metacarpal neck (6.2%), and proximal phalanx base (5.6%) fractures. No modifiable risk factors predicting the need for repeat reduction were identified. Conclusions: Some pediatric hand injuries are more likely to require repeat closed reduction by a hand surgeon. This retrospective study is the first step toward quality improvement as it provides opportunities for further research into the factors contributing to reductions that are unsuccessful at the first attempt. Identification of these factors and implementation of quality improvement measures are necessary to ensure the effective treatment of all pediatric hand injuries.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.245
Teacher spread0.237 · 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 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

Citations8
Published2019
Admission routes1
Has abstractyes

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