Medical graduates' early career choices of specialty and their eventual specialty destinations: UK prospective cohort studies
Bibliographic record
Abstract
OBJECTIVE To evaluate the rate of vitamin D in patients with neuromyelitis optica (NMO). BACKGROUND The vitamin D is known to act in the immune system by an immunomodulatory effect by regulating protein synthesis. The potential role of its deficiency as an environmental risk to develop multiple sclerosis (MS) is well known but its role in NMO is currently understudied. METHODS We performed a prospective multicenter study in France (Strasbourg) and in Morocco (Casablanca) evaluating the rate of 25-hydroxy-vitamin D in a cohort of NMO patients according to the 2006 Wingerchuk criteria comparing to a population of MS patients and a population with a non-neurological systemic autoimmune disease: the systemic sclerosis. RESULTS The mean rate of 25-hydroxy-vitamin D in NMO patients (n=31) was of 19.5 ng/ml that is a decrease of 35% compared to the lower limit of normal rate (30 to 80 ng/ml) and 25 patients (80,6%) with NMO had a rate less than 30 ng/ml. Mean rate of 25-hydroxy-vitamin D was 19,6 ng/ml in MS patients (n=55) and 17.7 ng/ml in patients with systemic sclerosis (n=22). 44 MS patients (80%) and 18 patients (81.8%) with systemic sclerosis had a rate less than 30 ng/ml. The difference between the rate of 25-hydroxy-vitamin D of NMO patients was not significantly different from this of patient with MS or systemic sclerosis. There was no difference between the mean rate of 25-hydroxy-vitamin D of French or Moroccan patients. DISCUSSION This study shows a vitamin D deficiency in patients with NMO both in France and in Morocco but a similar rate as in the patients with MS or with systemic sclerosis. The role of vitamin D deficiency in the development of autoimmune diseases and precisely in NMO has yet to be clarified, but a screening of deficiency and a vitamin D supplementation appears to be useful in these patients, particularly because of the high doses of corticosteroids with which they can be treated. <b>Disclosure:</b> Dr. Kremer has nothing to disclose. Dr. Slassi Sennou has nothing to disclose. Dr. Benkirane has nothing to disclose. Dr. Araqi Houssaini has nothing to disclose. Dr. Collongues has nothing to disclose. Dr. Chanson has nothing to disclose. Dr. Blanc has received personal compensation for activities with Biogen Idec, Novartis, Janssen-Cilag, Eisai Inc., Bayer Schering Pharma, Teva Neuroscience, Merck & Co. Inc., and Sanofi-Aventis Pharmaceuticals, Inc. Dr. Fleury has nothing to disclose. Dr. Theulin has nothing to disclose. Dr. Javier has nothing to disclose. Dr. De Seze has nothing to disclose.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".