Comorbidities of Children with Intellectual Disabilities
Bibliographic record
Abstract
The article presents a review of literature about health of children with intellectual disabilities (ID), as well as the results of own research. Altogether 73 children with intellectual disabilities of primary school age were involved in research process. In process their health groups and characteristic of comorbidity were described. Revealed that the majority of children surveyed were from group II health (63%) children with health group III accounted for 34% of children with health group IV-3%. In 33% of children with intellectual disabilities were observed these types of neurological disorders like hypertension-hydrocephalic syndrome (50%), microcephaly (17%), inorganic enuresis (13%), hydrocephalus (8%), convulsions (4%), hemiparesis (4%), asthenic-neurotic syndrome (4%). If you stay at the co-morbidity in children with intellectual disabilities, they were registered in 48 (66%) children, which is about 2/3 of all surveyed. It is typical for children with intellectual disabilities and is consistent with the literature. The most frequent chronic inflammatory diseases, moreover observed heart disease, eye disease, deficiency conditions, genitourinary system diseases, musculoskeletal and digestive systems, hearing disorders, skin disorders. To sum up, the conclusions that requires special attention to the health of children with intellectual disabilities and comprehensive measures to strengthen it, as well as the need for further such research.
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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 | 0.002 |
| 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) | 0.002 | 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".