Training attention control of very preterm infants: protocol for a feasibility study of the Attention Control Training (ACT)
Bibliographic record
Abstract
BACKGROUND: Children born preterm may display cognitive, learning, and behaviour difficulties as they grow up. In particular, very premature birth (gestation age between 28 and less than 32 weeks) may put infants at increased risk of intellectual deficits and attention deficit disorder. Evidence suggests that the basis of these problems may lie in difficulties in the development of executive functions. One of the earliest executive functions to emerge around 1 year of age is the ability to control attention. An eye-tracking-based cognitive training programme to support this emerging ability, the Attention Control Training (ACT), has been developed and tested with typically developing infants. The aim of this study is to investigate the feasibility of using the ACT with healthy very preterm (VP) infants when they are 12 months of age (corrected age). The ACT has the potential to address the need for supporting emerging cognitive abilities of VP infants with an early intervention, which may capitalise on infants' neural plasticity. METHODS/DESIGN: The feasibility study is designed to investigate whether it is possible to recruit and retain VP infants and their families in a randomised trial that compares attention and social attention of trained infants against those that are exposed to a control procedure. Feasibility issues include the referral/recruitment pathway, attendance, and engagement with testing and training sessions, completion of tasks, retention in the study, acceptability of outcome measures, quality of data collected (particularly, eye-tracking data). The results of the study will inform the development of a larger randomised trial. DISCUSSION: Several lines of evidence emphasise the need to support emerging cognitive and learning abilities of preterm infants using early interventions. However, early interventions with preterm infants, and particularly very preterm ones, face difficulties in recruiting and retaining participants. These problems are also augmented by the health vulnerability of this population. This feasibility study will provide the basis for informing the implementation of an early cognitive intervention for very preterm infants. TRIAL REGISTRATION: Registered Registration ID: NCT03896490. Retrospectively registered at Clinical Trials Protocol Registration and Results System (clinicaltrials.gov).
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.013 |
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".