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Low Serum Cholesterol Concentration and Risk of Suicide

2001· article· en· W2022149167 on OpenAlexaffabout
Larry F. Ellison, Howard Morrison

Bibliographic record

VenueEpidemiology · 2001
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsHealth Canada
Fundersnot available
KeywordsQuartileConfidence intervalMedicineLiterDemographyNational Health and Nutrition Examination SurveyCholesterolDepression (economics)Poison controlTotal cholesterolInternal medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Recent reports have suggested a link between low serum total cholesterol and risk of death from suicide. We examined this association using participants in the 1970-1972 Nutrition Canada Survey. We determined the mortality experience of Nutrition Canada Survey participants older than 11 years of age at baseline through 1993 by way of record linkage to the Canadian National Mortality Database. The relation between low serum total cholesterol and mortality from suicide was assessed using a stratified analysis (N = 11,554). There were 27 deaths due to suicide. Adjusting for age and sex, we found that those in the lowest quartile of serum total cholesterol concentration (<4.27 mmol/liter) had more than six times the risk of committing suicide (rate ratio = 6.39; 95% confidence interval = 1.27-32.1) as did subjects in the highest quartile (>5.77 mmol/liter). Increased rate ratios of 2.95 and 1.94 were observed for the second and third quartiles, respectively. The effect persisted after the exclusion from the analysis of the first 5 years of follow-up and after the removal of those who were unemployed or who had been treated for depression. These data indicate that low serum total cholesterol level is associated with an increased risk of suicide.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.336
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2001
Admission routes2
Has abstractyes

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