Mechanism of neurodevelopment of children with hearing deprivation
Bibliographic record
Abstract
100 children with sensorineural hearing loss, who had confirmed hearing impairment, were examined by routine somatoneurological and surdological survey, as well as standard psychomotor development scales (Alberta Infant Motor Scale (AIMS), Denver Developmental Screening Test (DDST), Griffiths Mental Development Scales (GMDS, GMDS-ER) in the city rehabilitation center for children with hearing and speech pathology. Criterion for inclusion in the study were the following: post-conceptual age of the child had to be not more than 36 months at the time of observation; bilateral or unilateral chronic sensorineural hearing loss or deafness and/or the presence of auditory neuropathy, confirmed by objective methods with modern audiological examinations for the core group; the absence of hearing loss, set by the modern objective methods of audiological examination for the comparison group; assessment of psychomotor and speech development of a child with at least two scales from the selection below. The survey results were analyzed using nonparametric statistical methods. Next Bayesian networks were used as efficient, compact, and intuitive way to represent knowledge related to uncertainty. Patterns of mental development, motor skills, speech and social functions in children with hearing impairment were obtained. The technique, which, depending on the factors affecting the trait under study, can help to predict with certainty the likelihood of various normal and deviant development of the child.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".