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Record W2965414758 · doi:10.1016/j.jpeds.2019.06.065

Predictors of Severe Neurologic Injury on Ultrasound Scan of the Head and Risk Factor-based Screening for Infants Born Preterm

2019· article· en· W2965414758 on OpenAlexafffund
Marc Beltempo, Pia Wintermark, Brigitte Lemyre, Wissam Shalish, Andrea Martel-Bucci, Michael Narvey, M Guillot, Prakesh S. Shah, Jaideep Kanungo, Joseph Ting, Zenon Cieslak, Rebecca Sherlock, Wendy Yee, Jennifer Toye, Carlos Fajardo, Zarin Kalapesi, Koravangattu Sankaran, Sibasis Daspal, Mary Seshia, Ruben Alvaro, Amit Mukerji, Orlando da Silva, Chuks Nwaesei, Kyong‐Soon Lee, Michael Dunn, Kimberly Dow, Ermelinda Pelausa, Keith J. Barrington, Anie Lapoint, Christine Drolet, Martine Claveau, Valérie Bertelle, Édith Massé, Roderick Canning, Hala Makary, Cecil Ojah, Luis Monterrosa, Julie Emberley, Jehier Afifi, Andrzej Kajetanowicz, Shoo K. Lee

Bibliographic record

VenueThe Journal of Pediatrics · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of TorontoMount Sinai HospitalHealth Sciences CentreMcGill University Health CentreSunnybrook Health Science CentreMontreal Children's HospitalUniversity of ManitobaChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health ResearchMinistry of Health, Ontario
KeywordsMedicineUltrasoundRisk factorHead injuryHead (geology)Head traumaPediatricsSurgeryRadiologyInternal medicine

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)0.000
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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

Citations20
Published2019
Admission routes2
Has abstractno

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