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Record W2600492335 · doi:10.1177/1352458517702553

Multiple sclerosis and suicide

2017· review· en· W2600492335 on OpenAlexaff
Anthony Feinstein, Bennis Pavisian

Bibliographic record

VenueMultiple Sclerosis Journal · 2017
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePsychiatrySuicide preventionPoison controlPopulationInjury preventionDepression (economics)EpidemiologyMultiple sclerosisContext (archaeology)Clinical psychologyPsychologyMedical emergencyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Mortality rates are elevated in people with multiple sclerosis (MS) relative to the general population. There is, however, some uncertainty whether suicide contributes to this. Epidemiological data suggest that the standardized mortality ratio (SMR) for suicide in MS is approximately twice that of the general population with younger males in the first few years following diagnosis most at risk. Rates of suicidal intent, a potential harbinger of more self-destructive behavior, are also elevated, but the frequency with which intent is followed by suicide is not known. Depression, severity of depression, social isolation, and alcohol abuse are associated with thoughts of suicide. The variables linked with suicide and suicidal intent are therefore well defined and should be readily available from routine clinical inquiry. While vigilance on the part of clinicians is required, particularly in the context of high-risk patients, it is also recognized that prevention is dependent on full disclosure of intent.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.452
GPT teacher head0.413
Teacher spread0.039 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations98
Published2017
Admission routes1
Has abstractyes

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