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Record W1751909794 · doi:10.3233/npm-15814128

Retinopathy of prematurity: Risk factors and variability in Canadian neonatal intensive care units

2015· article· en· W1751909794 on OpenAlexafffundabout
Kyle J. Thomas, Prakesh S. Shah, Roderick Canning, Adele Harrison, Seungwoo Lee

Bibliographic record

VenueJournal of Neonatal-Perinatal Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicRetinopathy of Prematurity Studies
Canadian institutionsVictoria General HospitalMoncton HospitalMount Sinai HospitalKingston General Hospital
FundersCanadian Institutes of Health Research
KeywordsRetinopathy of prematurityMedicineGestational ageBirth weightPediatricsRetrospective cohort studyDuctus arteriosusSepsisIntensive careCohortIntraventricular hemorrhageCohort studyPopulationLow birth weightIntensive care medicineInternal medicinePregnancy

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify predictors of severe retinopathy of prematurity (ROP) in a large population-based cohort and to examine risk-adjusted variations across units. STUDY DESIGN: Retrospective analysis of Canadian Neonatal Network data on neonates with birth weight <1500 g who were screened for ROP between 2003 and 2010. Characteristics of infants with and without ROP were compared and a risk-adjusted model for severe ROP was developed. Rates of severe ROP were compared between sites. RESULTS: 1163 of 9187 (12.7%) infants developed severe ROP. Lower gestational age, male sex, small for gestational age, patent ductus arteriosus, late onset sepsis, more than two blood transfusions, inotrope use, and outborn status were associated with an increased risk of severe ROP. Severe ROP rates varied significantly between units. CONCLUSION: Younger, smaller and sicker male infants had higher adjusted risks of severe ROP and rates varied significantly among sites.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.071
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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

Citations71
Published2015
Admission routes3
Has abstractyes

Explore more

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