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Record W2006458085 · doi:10.1017/s0317167100013688

Traumatic Injury and Multiple Sclerosis: A Systematic Review and Meta-Analysis

2013· review· en· W2006458085 on OpenAlexaffvenueabout
Sharon Warren, Susan Armijo Olivo, Jorge Fuentes, Karen Turpin, Douglas P. Gross, Linda Carroll, Kenneth G. Warren

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsInstitute for Work & HealthUniversity of Alberta
Fundersnot available
KeywordsMedicineOdds ratioMeta-analysisCINAHLCohort studyConfidence intervalMEDLINEIncidence (geometry)Internal medicinePhysical therapyPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

A systematic review/meta-analysis of literature addressing a possible association between traumatic injury and onset of multiple sclerosis was conducted. Medline, Embase, Cochrane DSR, Ovid HealthStar, CINAHL, ISI Web of Science and Scopus were searched for analytical studies from 1950 to 2011. Two investigators independently reviewed articles for inclusion, assessing their quality using the Newcastle-Ottawa Scale. Of the 13 case-control studies included, 8 were moderate quality and 5 low; of the 3 cohort studies 2 were high and 1 moderate. Meta-analysis including moderate and low quality case-control studies produced a modest but significant odds ratio: 1.41 (95% confidence interval: 1.03, 1.93). However, when low quality studies were excluded, the resulting odds ratio was non-significant. Cohort studies produced a non-significant standardized incidence ratio of 1.00 (95% confidence interval: 0.86, 1.16). These findings support the conclusion that there is no association between traumatic injury and multiple sclerosis onset; more high quality cohort studies would help to confirm this observation.

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)0.015
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.035
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.031
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.295
GPT teacher head0.380
Teacher spread0.085 · 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 designMeta-analysis
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

Citations27
Published2013
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicMultiple Sclerosis Research StudiesFrench-language works237,207