91: The Canadian Neonatal Follow-Up Network
Bibliographic record
Abstract
The Canadian Neonatal Follow-Up Network (CNFUN), a collaboration between all 26 Neonatal and Perinatal Follow-up Programs in Canada, was developed in liaison with the Canadian Neonatal Network (CNN) to facilitate collaboration in research, integrated data collection, knowledge translation and to improve the quality of care and long term outcomes of children seen in their programs. CNFUN implemented a standardized assessment at 18 months corrected age (CA) and a three year CA questionnaire for all survivors <29 weeks gestational age starting with births April 1, 2009 onwards. Describe the cohort of CNFUN preterm subjects born April 1, 2009 to July 1, 2011 and major adverse outcomes. NICUs notified local follow-up programs of eligible subjects. Patients were evaluated according to a standardized protocol and data manual. Bayley -III assessors completed online training specific for this study. Deidentified data was uploaded. Data was extracted from the CNFUN database to calculate cerebral palsy, hearing and visual impairment rates and outcomes on the Bayley -III cognitive, motor and language scores. Linkage with the Canadian Neonatal Network is planned. Of 2528 infants, 2109 were seen at 18 months CA. Subjects with missing information: CP status 45, Bayley III-cognitive 151, Bayley III language 210, Bayley III motor 222, hearing 139 and vision 59. Neurodevelopmental outcomes confirm a relatively low incidence of severe adverse outcomes but a significant percentage with scores <85 on the Bayley-III. This large CNFUN cohort of preterm survivors is a promising source of data for further analyses.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".