Bibliographic record
Abstract
Epilepsy has been associated with an increased fracture risk by various studies in the literature. This is attributed to the decreased bone mineral density (BMD) linked to the anti–epileptic drugs (AEDs) that are used to control seizures. As increasing age is also associated with decreasing BMD, older adults with epilepsy are at particular risk for fracture. There is no consensus on managing this risk and many epilepsy care guidelines do not address the risk at all. This is understandable considering the lack of adequate research on counteractive measures in this specific population. Moreover, though a prevailing theory explains the observed decreased BMD to be a result of AEDs reducing active vitamin D levels, the efficacy of vitamin D supplementation as a protective measure does not have robust evidence nor is there an agreed upon treatment regimen. The proposed project will aim to fill the gaps in the research and establish groundwork for future research on prevention and treatment by: examining the existing literature on vitamin D supplementation for bone health in adults with epilepsy; investigating the prevalence of osteoprotective behaviours and their impact on fracture occurrence in older Canadian adults with and without epilepsy; and determining the prevalence of vitamin D supplementation in the older adult Canadian epileptic population.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".