OPTIMIZING VITAMIN D LEVELS IN PATIENTS WITH MULTIPLE SCLEROSIS
Bibliographic record
Abstract
Background Emerging research suggests that vitamin D plays animportant role beyond bone health, particularly in immune function and may beimportant in people with Multiple Sclerosis (MS). MS clinic physicians in Calgary, Alberta believe that patients should have at least minimallysufficient ( > 80nmol/L) serum 25(OH)D levels to maintain adequate bone health. Involving patients in the assessment and management of their ownvitamin D needs may be effective and more efficient than having clinicians track levels. Objectives Determine the prevalence of vitamin D insufficiency and the feasibility of using different management methods to optimize serum25(OH)D levels. Methods 213 patients who attended the Calgary MS Clinicbetween September 2006 and January 2007 participated in this study. Eachpatient agreed to have serum 25(OH)D levels measured, and to adjust theirvitamin D dose according to an algorithm that they would follow or that would be used by a graduate student to recommend dose changes at baseline, 3- and 6-months. Results Mean age was 45.6 years (range 21-72); 78.9% werewomen. Mean EDSS was 3.2 (range 0-8.5). Mean baseline serum level was 72.8nmol/L (SD 26.8) (range 17.9-160.0); 62.4% had levels < 80 nmol/L. 60.6% of subjects were taking at least 1000 IU. Conclusions We found a high prevalence of vitamin D insufficiency despite a sizeable proportion taking at least 1000 IU of vitamin D3 daily. Six month data, including adherence to the study protocol and proportion ofparticipants with optimized 25(OH)D levels will be presented. Insight into themanagement of dosing for patients could have an impact on the integration ofvitamin D optimization into the MS 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".