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Record W2800867286 · doi:10.1097/ogx.0000000000000553

Survival in Very Preterm Infants: An International Comparison of 10 National Neonatal Networks

2018· article· en· W2800867286 on OpenAlexaff
Kjell Helenius, Gunnar Sjörs, Prakesh S. Shah, Neena Modi, Brian Reichman, Naho Morisaki, Satoshi Kusuda, Kei Lui, Brian A. Darlow, Dirk Bassler, Stellan Håkansson, Mark Adams, Máximo Vento, Franca Rusconi, Tetsuya Isayama, Shoo K. Lee, Liisa Lehtonen

Bibliographic record

VenueObstetrical & Gynecological Survey · 2018
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGeneral partnershipPediatricsFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

(Abstracted from Pediatrics 2017;140(6); doi: 10.1542/peds.2017–1264) Current literature demonstrates significant variation in the outcomes and survival rates of preterm infants in different health care settings and countries. The International Network for Evaluating Outcomes of Neonates (iNeo) is a nonprofit partnership consisting of 10 national and regional neonatal networks that seeks to study and improve outcomes of very preterm infants through comparison of care practices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.437
Teacher spread0.300 · 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 teacher head, not a consensus.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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