Evaluation of Decision Rules for Referring Women for Bone Densitometry by Dual-Energy X-ray Absorptiometry
Bibliographic record
Abstract
CONTEXT: Identification of women with low bone mineral density (BMD) is an important strategy in reducing the incidence of osteoporotic fractures. However, screening all women is not recommended. OBJECTIVES: To assess the diagnostic properties of 4 decision rules--Simple Calculated Osteoporosis Risk Estimation (SCORE), Osteoporosis Risk Assessment Instrument (ORAI), Age, Body Size, No Estrogen (ABONE), and body weight less than 70 kg (weight criterion)--for selecting women for dual-energy x-ray absorptiometry (DXA) testing and to compare results with recommendations made in the National Osteoporosis Foundation (NOF) practice guidelines. DESIGN AND SETTING: Analysis of data from the Canadian Multicentre Osteoporosis Study, a population-based community sample, collected from 9 study centers across Canada between February 1996 and September 1997. PARTICIPANTS: Postmenopausal women aged 45 years or older (N = 2365) without bone disease who had DXA data for the femoral neck, data to apply selection criteria, and who were not currently taking estrogens or who had been taking hormone replacement therapy for 5 or more years. MAIN OUTCOME MEASURES: Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve of each of the 4 decision rules and the NOF guidelines for identifying women with a BMD T score of less than -1.0 SD, less than -2.0 SD, and no more than -2.5 SD at the femoral neck, and percentages of women recommended for testing, stratified by BMD level and age. RESULTS: The percent of women with a BMD T score less than -1, less than -2, and no more than -2.5 were 68.3%, 25.4%, and 10.0%, respectively. The AUROC curves were greatest using SCORE and ORAI. The sensitivity for identifying women with a BMD T score of less than -2.0 was 93.7% (95% confidence interval [CI], 91.8%-95.6%) using the NOF guidelines and was 97.5% (95% CI, 96.3%-98.8%), 94.2% (95% CI, 92.3%-96.1%), 79.1% (95% CI, 75.9%-82.3%), and 79.6% (95% CI, 76.4%-82.8%), respectively, using the SCORE, ORAI, ABONE, and weight criterion. However, the NOF guidelines also resulted in 74.4% (95% CI, 71.3%-77.6%) of women with a normal BMD (T score of -1.0 or higher) being tested compared with 69.2% (95% CI, 65.9%-72.5%), 56.3% (95% CI, 52.7%-59.8%), 35.8% (95% CI, 32.4%-39.2%), and 38.1% (95% CI, 34.6%-41.6%), respectively, using the 4 decision rules. Assessments suggest that ABONE and weight criterion are not useful case-finding approaches. CONCLUSION: The SCORE and ORAI decision rules are better than the NOF guidelines at targeting BMD testing in high-risk patients. The acceptability of these rules in clinical practice merits further investigation given their potential effect on the use of densitometry services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".