Screening for Academic Risk Among Students With Cleft Lip and/or Palate: Patterns of Risk and Qualities of Effective Tools
Bibliographic record
Abstract
This study evaluated the effectiveness of academic screening measures in relation to parent-reported diagnoses. Multicenter, retrospective cohort study including structured interviews, questionnaires, and chart reviews. Six North American cleft centers. Children (n = 391) with cleft lip and/or palate, ages 8 to 10 years of age (192 male) and their guardians were recruited during regular clinic visits. Parent and child ratings on the Pediatric Quality of Life Inventory (PedsQL) School Scale, child report on CleftQ School Scale, parent report on the Adaptive Behavior Assessment System-Third Edition Functional Academics (ABAS-FA) Scale and Child Behavior Checklist (CBCL) School Competency Scale, parent interview, and medical chart review. Risk for concerns ranged from 12% to 41%, with higher risk reflected on the CBCL-SC compared to other measures. Males with cleft palate were consistently at the highest risk. Only 9% of the sample had a parent-reported diagnosis of a learning or language disability. Ratings from the ABAS-FA and CBCL-SC had the highest utility in identifying those with language and/or learning concerns. As cleft teams work to develop standardized batteries for screening and monitoring of patients, it is important to evaluate the effectiveness of measures in identifying those at highest risk. When screening for language and learning disorders, questions related to potential academic struggles, such as increased school effort or increased school distress, are most useful. Referrals for follow-up evaluation are recommended for those identified at high risk.
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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 | 0.007 | 0.045 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".