How to measure patient and family important outcomes in extremely preterm infants: A scoping review
Bibliographic record
Abstract
AIM: Parents of children born preterm have identified outcomes to be measured for audit and research at 18-24 months of age: child well-being, quality of life/function, socio-emotional/behavioural outcomes, respiratory, feeding, sleeping, and caregiver mental health. The aim was to identify the best tools to measure these seven domains. METHODS: Seven working groups completed literature reviews and evaluated potential tools to measure these outcomes in children aged 18-24 months. A group of experts and parents voted on the preferred tools in a workshop and by questionnaire. Consensus was 80% agreement. RESULTS: Consensus was obtained for seven brief, inexpensive, parent friendly valid measures available in English or French for use in a minimum dataset and potential alternative measures for use in funded research. CONCLUSION: Valid questionnaires and tools to measure parent-identified outcomes in young preterm children exist. This study will facilitate research and collection of data important to families.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".