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Record W1938088132 · doi:10.1093/pch/11.5.267

Neonatal follow-up programs and follow-up studies: Historical and current perspectives

2006· article· en· W1938088132 on OpenAlexaffabout
Reg Sauve, Shoo K. Lee

Bibliographic record

VenuePaediatrics & Child Health · 2006
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsAuditMedicineIntensive carePediatricsNeonatal intensive care unitIntensive care medicineNursingBusiness

Abstract

fetched live from OpenAlex

The present report reviews some highlights in the history of neonatal intensive care and neonatal follow-up programs, particularly developments and reports that were based on experiences in Canada. Early outcomes reported from 'preemie baby units' were distressing, but attention has consistently been paid to preterm infant outcomes, even from the early days of neonatal intensive care units. Most current follow-up programs have goals related to 'audit' functions, education and clinical roles, but existing literature related to these functions is limited. Several reports have provided guidance in terms of neonatal follow-up research issues, and these strengthen the place of follow-up studies in outcomes research.

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 imitation

Not 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.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.099
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.012
Science and technology studies0.0030.008
Scholarly communication0.0080.008
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.023
GPT teacher head0.293
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2006
Admission routes2
Has abstractyes

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