Musculoskeletal Injuries and Conditions Among Homeless Patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".