Reliability and validity of the Mini-Eating and Drinking Ability Classification System (Mini-EDACS) among Dutch preschoolers with cerebral palsy
Bibliographic record
Abstract
Purpose This study aimed to translate the English version of the Mini-Eating and Drinking Ability Classification System (Mini-EDACS) into Dutch and assess its psychometric properties and applicability among preschool-aged children with cerebral palsy (CP) in the Netherlands. Methods Forty-eight children with CP (18–36 months) were included. Inter-rater reliability of the Dutch version of the Mini-EDACS was assessed between two speech and language therapists (SLTs) and between two SLTs and parents. Construct validity was established by hypothesis testing regarding the expected strength of the correlation between Mini-EDACS level and sum score of (a) the Pediatric Eating Assessment Tool (PEDI-EAT-10) and (b) the Montreal Children's Hospital Feeding Scale (MCH-FS). Results The level of agreement for Mini-EDACS level was almost perfect between SLTs (weighted kappa (k w ) = 0.83) and substantial between parents and SLTs (parents vs SLT-1: k w = 0.77; parents vs SLT-2: k w = 0.70). Kendall's tau-b correlation between Mini-EDACS and PEDI-EAT-10 was 0.66 (p < 0.001), slightly lower than hypothesized, and 0.52 (p < 0.001) between Mini-EDACS and MCH-FS, aligning with the hypothesis. Applicability was found to be good. Conclusion The Dutch version of the Mini-EDACS showed sufficient inter-rater reliability, construct validity and applicability and can be used in clinical care in the Netherlands to promote unambiguous communication between healthcare professionals and parents.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".