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Musculoskeletal Injuries and Conditions Among Homeless Patients

2021· article· en· W3216768607 on OpenAlexaboutno aff

Bibliographic record

VenueJAAOS Global Research and Reviews · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionEmergency departmentPublic healthHealth careSuicide preventionOccupational safety and health

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this study was to analyze existing literature on musculoskeletal diseases that homeless populations face and provide recommendations on improving musculoskeletal outcomes for homeless individuals. METHODS: A comprehensive search of the literature was performed in March 2020 using the PubMed/MEDLINE (1966 to March 2020), Embase (1975 to April 2020), and CINHAL (1982 to 2020) databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used for accuracy of reporting, and the Newcastle-Ottawa Scale was used for quality assessment. RESULTS: Twenty-nine articles met inclusion criteria. Seven studies observed an increased prevalence of musculoskeletal injuries among the homeless population, four observed increased susceptibility to bacterial soft-tissue infection, four observed increased fractures/traumatic injuries, three described increased chronic pain, and six focused on conditions specific to the foot and ankle region. DISCUSSION: Homeless individuals often have inadequate access to care and rely on the emergency department for traumatic injuries. These findings have important implications for surgeons and public health officials and highlight the need for evidence-based interventions and increased follow-up. Targeted efforts and better tracking of follow-up and emergency department usage could improve health outcomes for homeless individuals and reduce the need costly late-stage interventions by providing early and more consistent care.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.530
Teacher spread0.423 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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