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Record W2171953635 · doi:10.1177/2050313x14561570

Vitamin D and depression: A case series

2014· article· en· W2171953635 on OpenAlexaff
Pallavi Nadkarni, Gbolahan Odejayi

Bibliographic record

VenueSAGE Open Medical Case Reports · 2014
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineDepression (economics)Series (stratigraphy)PsychiatryDermatology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.346
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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