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Record W2938294204 · doi:10.5435/jaaos-d-18-00618

Orthopaedic Injury Profiles in Methamphetamine Users: A Retrospective Observational Study

2019· article· en· W2938294204 on OpenAlexaff
Nicholas A. Trasolini, Hyunwoo P. Kang, John Carney, Alexis D. Rounds, Adam Murrietta, Geoffrey S. Marecek

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineRetrospective cohort studyTrauma centerInjury preventionPoison controlObservational studyEmergency medicineOccupational safety and healthSuicide preventionAccidentalMedical emergencyInternal medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: We sought to characterize the prevalence of methamphetamine (MA) abuse and associated orthopaedic injury patterns at our level 1 trauma center. METHODS: We conducted a retrospective review of all orthopaedic consults for the year 2016. Patients were classified as MA users based on urine toxicology results and social history. RESULTS: The prevalence of MA use was 10.0%. MA users were more likely to present with hand lacerations and other infections (P < 0.05 for all). Regarding the mechanism of injury, MA users were more likely to be involved in automobile versus pedestrian, automobile versus bicycle, ballistic, knife, closed fist, other assault/altercation, and animal bite injuries (P < 0.05 for all). DISCUSSION: MA use is prevalent at our level 1 trauma center. The prevalence and injury patterns of MA abuse warrant deeper study into the effects of this drug on orthopaedic outcomes. LEVEL OF EVIDENCE: Level 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.416
Teacher spread0.329 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

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