133HIP FRACTURES AT YANGON GENERAL HOSPITAL: A DRIVE TOWARDS INTEGRATED CARE
Bibliographic record
Abstract
Topic: Increasing incidence of fragility fractures worldwide poses significant health and economic burden. Incidence of hip fractures alone is set to increase to more than 6 million by 2020 (Kanis JA, 2007, p66). At Yangon General Hospital (YGH), fragility hip fractures accounted for 8% total orthopaedic admissions during a 4-month period. There is significant associated morbidity and mortality; around 10% of patients die in hospital within 1 month (Mithal A, Ebeling P, 2013, p8). This largely reflects the fact that patients are typically elderly with several comorbidities. Aim: to introduce integrated, multidisciplinary team working to benefit the care of hip fracture patients at YGH. Intervention: Analysis of existing hip fracture care (hip fracture database). Special Interest Group. Revision of clinical practice guidelines. Twice-weekly orthogeriatric ward rounds. Creation of an ‘Integrated Care Pathway for Hip Fracture Patients.’ Improvement: 305 patients were admitted between 10 January and 31 August 2017. Average time to surgery (TTS) reduced from 10 (range 1-15) to 8 (range 3-13) days. Average length of stay (LOS) remained 11 days (range 2-18 days in August) (patients discharged 2 days postoperatively). Promising results were seen for elderly patients. Between 1st March – 31st May, 54% patients were >70 years of age, 58% were managed surgically and TTS was 13 days. In June, 64% were managed surgically and TTS was 10 days. Regular special interest group meetings enabled a platform to identify care challenges and also the revision/generation of clinical practice guidelines such as echocardiogram requesting (previously done for all patients ≥60 years of age and incurring up to a 2-week preoperative delay) and osteoporosis assessment.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".