A real-world analysis of 1,823 hospitalized osteoporotic fractures in Northeast China
Bibliographic record
Abstract
Context: There are limited real-world data evidence assessing the clinical characteristics of hospitalized osteoporotic fractures in China. Objective: To investigate the clinical characteristics of hospitalized major osteoporotic fractures in Northeast China. Methods: We identified hospitalized fracture patients aged 50 and over from the First Affiliated Hospital of Jinzhou Medical University between January 1, 2018, and December 31, 2022. Major osteoporotic fractures including hip, vertebral, forearm and wrist, and humerus were diagnosed based on x-ray reports extracted from the electronic medical records (EMR). The cause of fracture, comorbidities, surgical methods, and anti-osteoporotic medications (AM) use were extracted from EMR. Results: The study population included 1823 fracture patients, 510 males and 1313 females. Over half of fracture patients were aged over 70 years. Hip fractures accounted for 68.4% of all fractures in males and 57.9% in females. For those with hip fractures, the most common sites were the femoral neck (62.9%) and intertrochanteric (35.3%). Most fractures were due to falls (81.0% in males and 80.2% in females). The two most common comorbidities for males and females were hyperlipemia (45.9% vs. 47.1%) and hypertension (38.2% vs. 41.6%). Only 4.7% males and 8.6% females were treated with AM. Conclusions: Hip fractures, especially femoral neck fractures, accounted for the majority of osteoporotic fractures in a tertiary public hospital in Northeast China. Common comorbidities in these fracture patients were hyperlipemia and hypertension. There was a very low rate of AM use among these patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".