Neonatal follow-up programs in Canada: A national survey
Bibliographic record
Abstract
BACKGROUND: A 2006 Canadian survey showed a large variability in neonatal follow-up practices. In 2010, all 26 tertiary level Neonatal Follow-Up clinics joined the Canadian Neonatal Follow-Up Network (CNFUN) and agreed to implement a standardized assessment (including the Bayley Scales of Infant and Toddler Development-III (Bayley-III) at 18 months corrected age for children born < 29 weeks' gestation. It is unknown whether the variability in follow-up practices lessened as a result. OBJECTIVES: To describe the current status of neonatal follow-up services in Canada and changes over time. METHODS: A comprehensive online survey was sent to all tertiary level CNFUN Follow-up programs. Questions were based on previous survey results, current literature, and investigator expertise and consensus. RESULTS: Respondents included 23 of 26 (88%) CNFUN programs. All sites provide neurodevelopmental screening and referrals in a multidisciplinary setting with variations in staffing. CNFUN programs vary with most offering five to seven visits. Since 2006, assessments at 18 months CA increased from 84% to 91% of sites, Bayley-III use increased from 21% to 74% (P=0.001) and eligibility for follow-up was expanded for children with stroke, congenital diaphragmatic hernia and select anomalies detected in utero. Audit data is collected by > 80% of tertiary programs. CONCLUSION: Care became more consistent after CNFUN; 18-month assessments and Bayley-III use increased significantly. However, marked variability in follow-up practices persists.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".