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Record W1993231836 · doi:10.1017/s0317167100001517

The Epidemiology of Infantile Spasms

2001· article· en· W1993231836 on OpenAlexaffvenueabout
Paula Brna, Kevin Gordon, Joseph M. Dooley, Ellen Wood

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2001
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsIncidence (geometry)EpidemiologyMedicinePediatricsPopulationDemographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to estimate population based incidence rates for infantile spasms (IS) and to study our clinical impression that the incidence of IS has recently decreased in the Canadian Provinces of Nova Scotia and Prince Edward Island. METHODS: Birth cohorts from 1978 to 1998, identified through the hospital health records, EEG records and physician computerized databases, were followed for two years for the development of IS. Disease incidence rates were calculated using denominators derived from Statistics Canada's reported annual live birth rates. RESULTS: The inclusion criteria for IS were fulfilled by 75 patients. The overall incidence of IS was 30.7/100,000 live births (95% Cl 24.3, 38.8). Etiologic classification was symptomatic for 51 cases (68%), cryptogenic for 18 (24%), and idiopathic in six children (8%). Although there were more males (N=44) than females (N=31), the incidence rates were similar. There was a marked variability in annual and five-year incidence rates. CONCLUSIONS: Although the clinical characteristics of our patients were similar to other reported IS populations, the instability in IS incidence rates indicates a need for caution in interpreting smaller IS epidemiologic studies.

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)0.001
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 score0.257
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.340
Teacher spread0.272 · 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

Citations39
Published2001
Admission routes3
Has abstractyes

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