Latent Class Analysis of Neurodevelopmental Deficit After Exposure to Anesthesia in Early Childhood
Bibliographic record
Abstract
INTRODUCTION: Although some studies have reported an association between early exposure to anesthesia and surgery and long-term neurodevelopmental deficit, the clinical phenotype of children exposed to anesthesia is still unknown. METHODS: Data were obtained from the Western Australian Pregnancy Cohort Study (Raine) with neuropsychological tests at age 10 years measuring language, cognition, motor function, and behavior. Latent class analysis of the tests was used to divide the cohort into mutually exclusive subclasses of neurodevelopmental deficit. Multivariable polytomous logistic regression was used to evaluate the association between exposure to surgery and anesthesia and each latent class, adjusting for demographic and medical covariates. RESULTS: In our cohort of 1444 children, latent class analysis identified 4 subclasses: (1) Normal: few deficits (n=1135, 78.6%); (2) Language and Cognitive deficits: primarily language, cognitive, and motor deficits (n=96, 6.6%); (3) Behavioral deficits: primarily behavioral deficits, (n=151, 10.5%); and (4) Severe deficits: deficits in all neuropsychological domains (n=62, 4.3%). Language and cognitive deficit group children were more likely to have exposure before age 3 (adjusted odds ratio [aOR], 2.11; 95% confidence interval [CI], 1.17-3.81), whereas a difference in exposure was not found between Behavioral or Severe deficit children (aOR, 1.00; 95% CI, 0.58-1.73, and aOR, 0.85; 95% CI, 0.34-2.15, respectively) and Normal children. CONCLUSIONS: Our results suggest that in evaluating children exposed to surgery and anesthesia at an early age, the phenotype of interest may be children with deficits primarily in language and cognition, and not children with broad neurodevelopmental delay or primarily behavioral deficits.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".