VA‐Based Survey of Osteoporosis Management in Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: Although osteoporosis is common following spinal cord injury (SCI), no guidelines exist for its treatment, diagnosis, or prevention. The authors hypothesized that wide variations in diagnosis and treatment practices result from the absence of guidelines. This study sought to characterize the diagnosis and management practices within the VA health care system for osteoporosis following SCI. DESIGN: Online survey regarding osteoporosis management in SCI composed of 27 questions designed to gather information on responder demographics, osteoporosis diagnostics, and treatment options. SETTING: VA health care system. PARTICIPANTS: VHA National SCI Staff Physicians and VHA National SCI Nurses (total n = 450) were sent an email with an invitation to participate. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Practice patterns were assessed, including factors associated with ordering a clinical workup and prescribing osteoporosis treatment. RESULTS: The response rate was 28%. Ninety-two prescribing practitioners (physicians, nurse practitioners, and physician assistants) were included in the analysis. Of these respondents, 50 (54%) prescribe medications for SCI-induced bone loss; 39 (42%) prescribe bisphosphonates and 46 (50%) prescribe vitamin D. There were 54 (59%) respondents who routinely order diagnostic tests, including dual energy x-ray absorptiometry scans in 50 (54%). Variations in practice were not explained by age, gender, or years practicing SCI medicine. Many respondents (23%) reported barriers to osteoporosis testing including lack of scanning protocols, cost, wheelchair inaccessibility of scanning facilities, and lack of effective treatment guidelines once osteoporosis is diagnosed. CONCLUSIONS: Despite an absence of screening and treatment guidelines, more than half of all respondents are actively diagnosing and treating osteoporosis with bisphosphonates within the VA health care setting. These data suggest that evidence-based practice guidelines are necessary to reduce practice variations and improve clinical care for this population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".