Factores asociados a epilepsia en niños en México: un estudio caso-control
Bibliographic record
Abstract
BACKGROUND: Epilepsy is the most common chronic neurological disease in the world. In Mexico, epilepsy is among the diseases more related to mortality due to non-infectious diseases in children. The objective of the study was to identify the factors associated with epilepsy in children entitled to the Mexican Social Security Institute (IMSS), in Acapulco, Mexico. METHODS: We carried out a case-control study from April 2010 to April 2011. We selected 118 cases from the database of outpatient pediatric neurology with epilepsy diagnostic with two year of evolution according to the International League Against Epilepsy criteria. We selected 118 controls from the same Medical Units where cases were detected. Data collected throughout an interview with the mothers included information on history of epilepsy among relatives, prenatal, perinatal and postnatal history. Bivariate and multivariate analysis was performed using Mantel-Haenszel process. RESULTS: Multivariate analysis identified three factors associated with epilepsy: family history of epilepsy in first-degree relatives (adjusted Odds ratio (ORa) 2.44, 95%CI 1.18 -5.03), birth asphyxia (ORa 2.20, 95%CI 1.16-34.18), and urinary tract infection in the prenatal stage (ORa, 1.80, 95%CI 1.0 - 3.24). CONCLUSIONS: Preventing birth asphyxia and urinary tract infections during pregnancy reduces the risk of epilepsy regardless of the history of epilepsy in first-degree relatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".