Anticonvulsant drug use and low bone mass in adults with neurodevelopmental disorders
Bibliographic record
Abstract
BACKGROUND: Use of anticonvulsant drugs among adults with neurodevelopmental disorders may be an important risk factor for both osteoporosis and skeletal fractures. AIM: To determine the relationship between anticonvulsant drug use and both low bone mass and bone fractures in such adults. DESIGN: Cross-sectional study of 273 adults with neurodevelopmental disorders, 40% of whom were receiving one or more anticonvulsant drugs. SETTING: Single Canadian long-term care facility. METHODS: Demographic data were abstracted from each resident's chart in a standardized manner, including body mass, degree of mobility, major falls within the previous 12 months, and all medications. Quantitative calcaneal ultrasonography was performed on each resident without knowledge of their current drug use. The Quantitative Ultrasound Index was employed to express 'bone stiffness'. Low bone mass was defined as a T-score 2.5 SDs below the norm for young healthy adults. RESULTS: Compared to non-users (15.5%), low bone mass was more prevalent among those taking either one (20.3%; OR 1.7, 95%CI 0.6-4.4) or two or more anticonvulsant agents (42.2%, OR 5.9, 95%CI 2.2-16.2). The risk of recent skeletal fractures was not significantly greater in those taking a single anticonvulsant than in non-users (28.6% vs. 21.6%; OR 1.0, 95%CI 0.4-2.8), but tended to be higher in those taking two or more (48.7%; OR 2.2, 95%CI 0.8-5.9). CONCLUSIONS: Adults with neurodevelopmental disorders residing in a long-term care facility have a high rate of both low bone mass and skeletal fractures, especially with concomitant use of anticonvulsant drugs. These individuals should be assessed for the presence of low bone mass, and may warrant prophylactic treatment against bone loss, including calcium and vitamin D supplementation.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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 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".