A Population-Based Study to Determine the Performance of the Cognitive Adaptive Test/Clinical Linguistic and Auditory Milestone Scale to Predict the Mental Developmental Index at 18 Months on the Bayley Scales of Infant Development-II in Very Preterm Infants
Bibliographic record
Abstract
OBJECTIVES: To determine optimal ages to perform the Cognitive Adaptive Test/Clinical Linguistic and Auditory Milestone Scale (CAT/CLAMS) and optimal "cutoff" score of the CAT/CLAMS to screen very preterm infants (<31 weeks) for severe cognitive-adaptive delay and to ascertain the sensitivity, specificity and likelihood ratios using optimal cutoff scores compared with the Mental Developmental Index (MDI) of the Bayley Scales of Infant Development II. METHODS: A population-based cohort of very preterm infants who were born to mothers who resided in Nova Scotia or Prince Edward Island were evaluated at 4, 8, 12, and 18 months' corrected gestational age, which included a CAT/CLAMS by a physician. At 18 months' corrected gestational age, each child was assessed using the Bayley Scales of Infant Development II, the "gold standard" for developmental delay in young infants. The results of each CAT/CLAMS was compared with the 18-month MDI to identify significant developmental delay (MDI <70). RESULTS: Optimal scores on the CAT/CLAMS to identify correctly MDI <70 were determined by using the kappa statistic for chance independent agreement. Sensitivities and specificities for optimal cutoff scores were as follows: 4-month score <109 (88% and 37%), 8-month score <98 (75% and 82%), 12-month score <81 (63% and 99%), and 18-month score <83 (88% and 98%). CONCLUSION: Sensitivity and specificity of the CAT/CLAMS are high in very preterm infants at identifying major developmental delay at 12 and 18 months. For follow-up programs without psychology services, the CAT/CLAMS at 12 and 18 months is a reasonable screening tool to determine which children need expedited psychology referral for cognitive delay.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".