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Record W2788435418 · doi:10.1186/s12887-017-0978-6

Caffeine is a risk factor for osteopenia of prematurity in preterm infants: a cohort study

2018· article· en· W2788435418 on OpenAlexaff
Ebtihal Ali, Cheryl R. Greenberg, Michael Moffatt, Michael Narvey, Martin H. Reed, Depeng Jiang

Bibliographic record

VenueBMC Pediatrics · 2018
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of ManitobaHealth Sciences CentreManitoba HealthWinnipeg Regional Health Authority
Fundersnot available
KeywordsMedicineOsteopeniaApnea of prematurityCohortPediatricsRetrospective cohort studyBirth weightCohort studyLow birth weightRisk factorGestational ageObstetricsInternal medicinePregnancyOsteoporosisBone mineral

Abstract

fetched live from OpenAlex

BACKGROUND: Caffeine, the most commonly used medication in Neonatal Intensive Care Units, has calciuric and osteoclastogenic effects. METHODS: To examine the association between the cumulative dose and duration of therapy of caffeine and osteopenia of prematurity, a retrospective cohort study was conducted including premature infants less than 31 weeks and birth weight less than 1500 g. Osteopenia of prematurity was evaluated using chest X-rays on a biweekly basis over 12 weeks of hospitalization. RESULTS: The cohort included 109 infants. 51% had osteopenia of prematurity and 8% had spontaneous rib fractures. Using the generalized linear mixed model, caffeine dose and duration of caffeine therapy showed a strong association with osteopenia of prematurity. Steroids and vitamin D were also significantly correlated with osteopenia of prematurity while diuretic use did not show a statistically significant effect. CONCLUSION: The cumulative dose and duration of therapy of caffeine, as well as steroid are associated with osteopenia of prematurity in this cohort. Future studies are needed to confirm these findings and determine the lowest dose of caffeine needed to treat effectively apnea of prematurity.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.391
Teacher spread0.342 · 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.

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

Citations48
Published2018
Admission routes1
Has abstractyes

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