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Record W2519657095 · doi:10.1016/s1473-3099(16)30082-2

Strengthening the Reporting of Observational Studies in Epidemiology for Newborn Infection (STROBE-NI): an extension of the STROBE statement for neonatal infection research

2016· review· en· W2519657095 on OpenAlexaff
Elizabeth Fitchett, Anna C. Seale, Stefania Vergnano, Paul T. Heath, Samir K. Saha, Ramesh Agarwal, Adejumoke Idowu Ayede, Zulfiqar A Bhutta, Robert E. Black, Kalifa Bojang, Harry Campbell, Simon Cousens, Gary L. Darmstadt, Shabir A. Madhi, Ajoke Sobanjo-ter Meulen, Neena Modi, Janna Patterson, Shamim Qazi, Stephanie J. Schrag, Barbara J. Stoll, Stephen Wall, Robinson D. Wammanda, Joy E Lawn

Bibliographic record

VenueThe Lancet Infectious Diseases · 2016
Typereview
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Institute for Health and Care ResearchWorld Health Organization
KeywordsStrengthening the reporting of observational studies in epidemiologyObservational studyChecklistEpidemiologyMedicinePsychological interventionIncidence (geometry)Systematic reviewFamily medicineMEDLINEEnvironmental healthPediatricsPsychologyPathologyNursingBiology

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)0.465
metaresearch head score (Gemma)0.654
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.535
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4650.654
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0190.025
Bibliometrics0.0100.011
Science and technology studies0.0020.006
Scholarly communication0.0090.007
Open science0.0090.010
Research integrity0.0170.014
Insufficient payload (model declined to judge)0.0050.001

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 head0.624
GPT teacher head0.569
Teacher spread0.056 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations216
Published2016
Admission routes1
Has abstractno

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