Use of the Bayley Scales of Infant Development-III by therapists for assessing development and recommending treatment for infants in a NICU follow-up clinic
Bibliographic record
Abstract
Infants who have been hospitalized in a neonatal intensive care unit (NICU) may present with a multitude of challenges that put them at risk for delayed development. Early Intervention and specialized NICU follow up clinics are in place to help identify NICU graduates’ need for therapy services. Well-established, standardized assessments, such as the Bayley Scales of Infant and Toddler Development (BSID-III) are utilized by occupational and physical therapists when making recommendations for therapy. The purpose of this retrospective chart review (N=104) was to identify the extent to which BSID-III motor scores were predictive of a referral for further developmental therapy in infants who were seen in NICU follow-up and to examine how therapist clinical judgment related to BSID-III scores. Independent sample t-tests conducted to compare motor performance to recommendations for motor therapy found there was a significant difference in the gross motor scores for those who were and were not recommended for motor therapy. Quality, quantity, and variability of motor skills emerged as recurring themes in therapist’s clinical judgment for initiating motor therapy, despite BSID-III scores that were within normal limits. Findings from this study indicate that the factors that influence follow-up recommendations are complex and that test scores alone were not indicative of whether or not a referral was given. Information gathered from this study may help increase understanding of how BSID-III scores and clinical judgment relate for therapists recommending motor therapy for NICU graduates.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".