Yield of Laboratory Testing to Identify Secondary Contributors to Osteoporosis in Otherwise Healthy Women
Bibliographic record
Abstract
Our purpose in this study was to determine the prevalence of undetected disorders of bone and mineral metabolism in women with osteoporosis and to identify the most useful and cost-efficient screening tests to detect these disorders. A cross-sectional study was conducted among 664 postmenopausal women with osteoporosis at the Osteoporosis and Metabolic Bone Disease Program at the Mount Sinai Hospital in New York between January 1992 and June 1996. Women without a history of diseases or medications known to adversely affect bone who completed extensive laboratory testing including complete blood count, chemistry profile, 24-h urinary calcium, 25(OH)vitamin D, and PTH were included. Among 173 women who met the inclusion criteria for the study, previously undiagnosed disorders of bone and mineral metabolism were identified in 55 women (32%). Disorders of calcium metabolism and hyperparathyroidism were the most frequent diagnoses. A testing strategy involving measurement of 24-h urine calcium, serum calcium, and serum PTH for all women and serum TSH among women on thyroid replacement therapy would have been sufficient to diagnose 47 of these 55 women (85%) at an estimated cost of $75 per patient screened. Previously undiagnosed disorders affecting the skeleton are common in otherwise healthy women with low bone density. A simple testing strategy is likely to identify most such disorders.
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 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".