Bibliographic record
Abstract
INTRODUCTION: Over two-thirds of Canadians are deficient in vitamin D. Clinical overlap can compound diagnosis of depression in vitamin D deficient individuals. Citing high costs, the Ministry of Health has restricted routine vitamin D screening and hence is not feasible. OBJECTIVES: The current case series is an attempt to recognise the clinical overlap between depression and vitamin D deficiency in order to avoid unnecessary antidepressant prescriptions and to demonstrate the role of collaborative care in such patients. METHOD: After appropriate ethics approval 62 patients from an outpatient clinic were screened for the diagnosis of treatment resistant depression. Those who had predominant somatic complaints were further screened for organic factors and those with inadequate vitamin D levels were referred to family physicians for supplementation with vitamin D. RESULTS: More than 50% were detected deficient in vitamin D after our evaluation. They showed subjective improvement with vitamin D supplements. No modification of antidepressants was needed. CONCLUSION: Vitamin D deficiency should be suspected in depressed patients with prominent somatic symptoms and their treatment resistance should be reconsidered to avoid unnecessary exposure to mood stabilisers. Collaborating with primary care is advocated. LIMITATION: Co-prescription of an antidepressant is a confounder in our case series, and we propose more organised studies with objective rating scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| 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".