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Record W2320101097 · doi:10.1017/s0317167100009136

Epidemiology of Guillain-Barré Syndrome in the Province of Quebec

2008· article· en· W2320101097 on OpenAlexaffvenueabout
Geneviève Deceuninck, Renée‐Myriam Boucher, Philippe De Wals, Manale Ouakki

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2008
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsGuillain-Barre syndromeMedicineEpidemiologyPediatricsIncidence (geometry)PolyradiculopathyPopulationMedical recordDemographyEnvironmental healthSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In the province of Quebec, a population-based study of Guillain-Barré syndrome (GBS) was conducted at the time of a mass immunization campaign against meningococcal disease, in 2001. METHODS: The study population included residents aged 2 months to 20 years observed from November 1st, 2000 to December 31, 2002, representing 4,075,465 person-years of observation. GBS cases were identified in the provincial hospital database Med-Echo and medical records were reviewed. RESULTS: Thirty-three incident GBS cases were identified, including 27 cases of acute inflammatory demyelinating polyradiculopathy. The overall GBS incidence rate was 0.8/100,000 person-years, higher in persons aged 1 to 4 years (2.1/100,000) than in those 5 years or more (0.6/100,000). There was a female preponderance and no significant seasonal variation. All patients survived. CONCLUSION: Results could be used to interpret reports of adverse events associated with the introduction of new vaccines in this age-group in Canada.

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)0.002
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 score0.021
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.282
Teacher spread0.236 · 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

Citations7
Published2008
Admission routes3
Has abstractyes

Explore more

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