Disparities in fracture care in China: a cross-sectional analysis of the INORMUS study
Bibliographic record
Abstract
BACKGROUND: There is limited information on the morbidity burden and care pathway of musculoskeletal injuries in China. Here we describe the characteristics and care delivered for patients hospitalised for traumatic fracture in China. MATERIAL AND METHODS: Patients (≥18 years of age) from 12 hospitals across China hospitalised for traumatic injuries in the previous three months were recruited from October 2015 to April 2017 as part of a global multicentre prospective observational study (the INORMUS Study). RESULTS: Of 8,389 patients recruited (mean age 56.3 years; 51.5% male), hip fracture was the most common fracture (22.2%), then spine (16.9%), foot (13.1%), and tibia (10.4%). Falls were the most common injury mechanism (64.1%), followed by transport (22.0%). Most injuries occurred on the street (41.9%) or at home (34.6%). There was variation between hospitals across regions regarding the reasons for delays to care, time to surgery, and antibiotic usage. Around 70% of patients with hip fracture achieved definitive stabilisation of the fracture more than 48 hours from admission in study hospitals in the Western and Northern regions. CONCLUSION: Aligning with global patterns of injury, road traffic injury, falls, and hip fracture are major contributors to the injury burden in China. The significant variations in care identified highlight the need for enhanced and equitable fracture care, including improved community awareness of fracture care, introduction of practices to avoid over-treatment, publication of national guidelines for accelerated surgery and antibiotic prophylaxis, participation of multidisciplinary team, and establishment of a hospital-driven fast-track pathway and an auditing mechanism for quality improvement in fracture care in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".