Bibliographic record
Abstract
Feeding problems are common even in typically developing infants and children. However, they are more frequent and persistent in children with developmental disabilities. This article will provide an overview of current literature and a rationale underlying the interventions used for children with cerebral palsy (CP) who have eating impairments (dysphagia). The review is not intended to be exhaustive, but papers were selected that highlight some of the issues and challenges of the field. Normal oral-motor development is briefly discussed to show how it may inform clinical practice in the understanding of feeding problems. Description of the risk factors and the nature and extent of eating impairments will show how interventions need to be specific to the severity of eating impairments. Examination of sensorimotor therapies, using oral stimulation exercises or an intra-oral appliance, will highlight the range of their effectiveness, as well as their limitations. Similarly, an examination of tube feeding, used for nutritional rehabilitation of the most severely affected children, will address the benefits, controversies as well as moral issues encountered by caregivers and professionals. Multi-center studies will be needed to obtain more homogeneous samples, large enough to address questions of early interventions and their subsequent effect on later development.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".