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Record W2152909175 · doi:10.1176/appi.ajp.161.11.2123

Cholesterol Metabolism and Suicidality in Smith-Lemli-Opitz Syndrome Carriers

2004· article· en· W2152909175 on OpenAlexaff
Aleksandra Lalovic, Louise S. Merkens, Laura Russell, Geneviève Arsenault‐Lapierre, Małgorzata J.M. Nowaczyk, Forbes D. Porter, Robert D. Steiner, Gustavo Turecki

Bibliographic record

VenueAmerican Journal of Psychiatry · 2004
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsDouglas Mental Health University Institute
FundersNational Center for Research Resources
KeywordsSmith–Lemli–Opitz syndromePsychopathologyCholesterolHypocholesterolemiaPoison controlPsychiatryInternal medicineMedicineEndocrinologyPsychologyReductaseMedical emergency7-Dehydrocholesterol reductaseBiologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: The authors examined the relationship between cholesterol metabolism and suicidality in carriers of Smith-Lemli-Opitz syndrome and their families. This population has a partial deficiency in 7-dehydrocholesterol reductase (DHCR7), the enzyme that catalyzes the last step in cholesterol biosynthesis. METHOD: Suicidal behavior, depression, misuse of alcohol and drugs, and family history of psychopathology, including attempted or completed suicide, were assessed by structured interview in 51 carriers of Smith-Lemli-Opitz syndrome and 54 matched comparison subjects. RESULTS: There were significantly more suicide attempters and completers among the biological relatives of Smith-Lemli-Opitz syndrome carriers than comparison subjects, but family history of psychopathology did not significantly differ between the groups. More suicide attempts were reported among Smith-Lemli-Opitz syndrome carriers than among the comparison subjects. CONCLUSIONS: These results, based on a unique study design, provide additional evidence supporting the relationship between cholesterol metabolism and suicidal behavior.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.030
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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

Citations40
Published2004
Admission routes1
Has abstractyes

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