Improving care after hip fracture: the fracture? Think osteoporosis (FTOP) program
Bibliographic record
Abstract
BACKGROUND: Hip fractures are a common and serious consequence of osteoporosis, and hip fracture patients are at high risk for recurrence. Appropriate pharmacotherapy reduces this risk and is associated with reduced mortality after hip fracture, but a care gap exists for fracture prevention in these patients. This evaluation determined rates of osteoporosis treatment and bone mineral density (BMD) testing in hip fracture patients following discharge from a rehabilitation unit. METHODS: A prospective cohort study of hip fracture patients aged ≥ 50 on an inpatient rehabilitation unit in 2008 and 2011. Patients were seen by a nurse specialist, and encouraged to see their family physician for further assessment and treatment. Physicians were sent a letter indicating the need to follow up with their patient. Patients were contacted following discharge from hospital to determine treatment rates. RESULTS: Of 310 eligible hip fracture patients admitted to the rehabilitation unit in the years studied, 207 patients were reached post-discharge and provided data. Of patients who were not previously taking osteoporosis medication, 59% of patients from the 2008 cohort, and 42% of patients from the 2011 cohort had osteoporosis treatment initiated by six months following discharge. By 2 months following discharge, 46% of patients in the 2008 cohort had a new BMD performed or scheduled, while this was true for 14% of patients from the 2011 cohort. 35% of patients in 2011 had not seen their family physician by 2 months following discharge. CONCLUSIONS: Rates for osteoporosis treatment and BMD testing were higher than those reported in the literature for patients not enrolled in case manager programs. BMD testing declined from 2008 to 2011. Lower treatment rates may be due to concerns regarding reports of possible association between bisphosphonate use and atypical fractures. Improving rates of patient follow-up with family physicians will be important for increasing hip fracture treatment rates after discharge.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".