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Record W2161898875 · doi:10.5770/cgj.15.39

Hypovitaminosis D: A Contributor to Psychiatric Disorders in Elderly?

2012· article· en· W2161898875 on OpenAlexaffvenue
Jennifer Ford, Ana Hategan, James A. Bourgeois, Daniel K. Tisi

Bibliographic record

VenueCanadian Geriatrics Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineVitamin D and neurologyvitamin D deficiencyInternal medicineUnivariate analysisPopulationHypovitaminosisGastroenterologyPediatricsMultivariate analysis

Abstract

fetched live from OpenAlex

BACKGROUND: Hypovitaminosis D is unrecognized and remarkably common in geriatric patients, with various clinical manifestations. The purpose of this study was to prospectively assess the vitamin D (VD) status in newly admitted psychogeriatric patients, and to study the correlation of VD status with indicators of calcium metabolism. METHODS: A valid VD sample, as measured by serum 25-hydroxyvitamin D (25-OHD), was obtained from nine consecutive psycogeriatric inpatients (66% women), during a one-month period in 2011. The Research Ethics Boards at St. Joseph's Healthcare Hamilton approved this project. RESULTS: All participants showed VD inadequacy (defined as 25-OHD ≤ 75 nmol/L) with a mean level of serum 25-OHD of 45.5 ± 14.6 (range 28.5-73.4) nmol/L. None of the patients in the sample met criteria for VD deficiency (currently defined by expert consensus as 25-OHD < 25 nmol/L). Mean serum VD levels were lower in females (38.8 ± 9.8 nmol/L) than in males (59.0 ± 14.3 nmol/L), p = .03. Magnesium and PTH were both higher in females (p = .03 and .02, respectively). Univariate linear regression analysis showed that VD levels were strongly negatively associated with magnesium (p = .001) and PTH (p = .02). CONCLUSION: Since research links VD deficiency to psychiatric conditions, high rates of insufficiency in this population is very common and routine supplements are strongly suggested, regardless of patients' living environment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2012
Admission routes2
Has abstractyes

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