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Record W4379012664 · doi:10.1016/j.jpeds.2023.113531

Does Growth Velocity Affect Associations between Birth Weight and Neurodevelopment for Infants Born Very Preterm?

2023· article· en· W4379012664 on OpenAlexafffund
Thibaut Sériès, M Guillot, Georgina Angoa, Étienne Pronovost, Aissatou Bintou Khairy Thilor Ndiaye, Ibrahim Mohamed, David Simonyan, Pascal M. Lavoie, Anne Synnes, Isabelle Marc, Jehier Afifi, Julie Bartholomew, Georges Caouette, Zenon Cieslak, Cecilia de Cabo, Thierry Daboval, William D. Fraser, Leonora Hendson, Faiza Khurshid, Thierry Lacaze‐Masmonteil, Bodil Katrine Larsen, Brigitte Lemyre, Benoı̂t Mâsse, Édith Massé, Anne Monique Nuyt, François Olivier, Gustavo Pelligra, Thérèse Perreault, Mosarrat Qureshi, Chelsea Ruth, Lannae Strueby, Kamran Yusuf

Bibliographic record

VenueThe Journal of Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsCentre hospitalier de l'Université LavalUniversity of British ColumbiaCentre Hospitalier Universitaire Sainte-JustineUniversité LavalUniversité de MontréalCentre hospitalier universitaire de QuébecUniversité du Québec à Trois-Rivières
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCentre Hospitalier Universitaire de QuébecMichael Smith Health Research BCBC Children's HospitalFondation CHU de QuébecUniversité Laval
KeywordsMedicineAffect (linguistics)Birth weightGrowth velocityPediatricsLow birth weightPremature birthGestational agePregnancyInternal medicineGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.350
Teacher spread0.307 · 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

Citations9
Published2023
Admission routes2
Has abstractno

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