Accident or osteoporosis?: Survey of community follow-up after low-trauma fracture.
Bibliographic record
Abstract
OBJECTIVE: To describe the postfracture osteoporosis management of at-risk patients presenting with low-trauma fracture in a suburban community hospital setting. DESIGN: Telephone survey. SETTING: Hospital emergency department serving a retirement community in White Rock and South Surrey, BC. PARTICIPANTS: Men and women older than 40 years of age who presented with low-trauma fracture between October 1, 2004, and April 30, 2005. MAIN OUTCOME MEASURES: The prevalence of bone mineral density testing, osteoporosis medication prescriptions, referrals to fall prevention programs, and calcium and vitamin D supplementation within 6 months of the index fracture, as well as patient perceptions of future risk of fracture and sources of osteoporosis information. RESULTS: A total of 181 people met the eligibility criteria and 161 were contacted; 84 (52%) people responded, of whom 53 were interviewed. At the time of their index fractures, 79% (42 of 53) of patients surveyed were not taking osteoporosis medication. After the index fracture, 30% (16 of 53) received new bone mineral density testing, and 8% (4 of 53) were starting courses of new osteoporosis medication. Sixty-eight percent (36 of 53) of all patients were taking calcium supplements and 50% (26 of 53) were taking vitamin D supplements. Eight percent (4 of 53) of patients were referred to a fall prevention program and 9% (5 of 53) were prescribed hip protectors; 19% (10 of 53) of patients thought they were at risk of having another fracture. CONCLUSION: Osteoporosis management of patients after low-trauma fracture in this community was suboptimal; the role of the media, family and friends, and allied health professionals to prevent fractures in at-risk individuals needs to be further explored.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Survey of post-fracture osteoporosis management; the object is clinical care quality, not research practice.
The study evaluates osteoporosis follow-up in patients, not research practice.
Clinical survey of post-fracture osteoporosis care; object is patient management, not research practice.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".