Bone mineral density testing after fragility fracture
Bibliographic record
Abstract
Objective To determine the proportion of patients with fragility fractures who can be expected to have low bone mineral density (BMD) at the time of fracture and to assist FPs in deciding whether to refer patients for BMD testing. Data sources MEDLINE, EMBASE, and CINAHL were searched from the earliest available dates through September 2009. Study selection English-language articles reporting BMD test results of patients with fragility fractures who were managed in an orthopedic environment (eg, fracture clinic, emergency management by orthopedic surgeons, inpatients) were eligible for review. While the orthopedic environment has been identified as an ideal point for case finding, FPs are often responsible for investigation and treatment. Factors that potentially influenced BMD test results (eg, selection of fracture types, exclusion criteria) were identified. Studies with 2 or more selection factors of potential influence were flagged, and rates of low BMD were calculated including and excluding these studies. Synthesis The distribution of the proportion of persons with low BMD was summarized across studies using descriptive statistics. We calculated lower boundaries on this distribution, using standard statistical thresholds, to determine a lower threshold of the expected rate of low BMD. Conclusion Family physicians evaluating patients with fragility fractures can expect that at least two-thirds of patients with fragility fractures who are older than 50 years of age will have low BMD (T score ≤ −1.0). With this a priori expectation, FPs might more readily conduct a fracture risk assessment and pursue warranted fracture risk reduction strategies following fragility fracture.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".