Screening for language delay after life-saving therapies in term-born infants
Bibliographic record
Abstract
BACKGROUND: Strong recommendations have been made for the periodic developmental surveillance, screening, and evaluation of children with CHD. This supports similar calls for all at-risk children in order to provide timely, structured early developmental intervention that may improve outcomes. The aim of this study was to determine the accuracy of screening for language delay after life-saving therapies using the parent-completed vocabulary screen of the language Development Survey, by comparing screening with the individually administered language scores of the Bayley Scales of Infant and Toddler Development, Third edition. METHOD: In total, 310 (92.5%) of 335 eligible term-born children, born between 2004 and 2011, receiving complex cardiac surgery, heart or liver transplantation, or extracorporeal membrane oxygenation in infancy, were assessed at 21.5 (2.8) months of age (lost, 25 (7.5%)), through developmental/rehabilitation centres at six sites as part of the Western Canadian Complex Pediatric Therapies Follow-up Group. RESULTS: Vocabulary screening delay was defined as scores ⩽15th percentile. Language delay defined as scores >1 SD below the mean was calculated for language composite score, receptive and expressive communication scores of the Bayley-III. Delayed scores for the 310 children were as follows: vocabulary, 144 (46.5%); language composite, 125 (40.3%); receptive communication, 98 (31.6%); and expressive communication, 124 (40%). Sensitivity, specificity, positive predictive values, and negative predictive values of screened vocabulary delay for tested language composite delay were 79.2, 75.7, 68.8, and 84.3%, respectively. CONCLUSION: High rates of language delay after life-saving therapies are concerning. Although the screening test appears to over-identify language delay relative to the tested Bayley-III, it may be a useful screening tool for early language development leading to earlier referral for intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".