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Record W4377098170 · doi:10.5539/gjhs.v15n5p24

Effect of Seasonal Variation on the Bilirubin Content and Hematological Indices among Neonates in Southern Gaza, Palestine

2023· article· en· W4377098170 on OpenAlexvenueno aff
Mahmoud Mahmoud Elhabiby, Ayman Abu Mustafa, Abdelmarouf Mohieldein

Bibliographic record

VenueGlobal Journal of Health Science · 2023
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsBilirubinJaundiceHematocritMedicineHemoglobinPhysiologySeasonalityAnimal scienceInternal medicineBiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND & OBJECTIVES: Premature newborns’ bilirubin conjugation and excretion mechanisms are undeveloped. Seasonal changes and other variables affect the severity of newborns’ physiological jaundice. This study examined blood indices, bilirubin levels, and birth season in neonates in the southern Gaza Strip of Palestine. METHODS: A cross-sectional study of 366 neonates aged one to 14 days was conducted in Medical Nasser Complex-Southern Gaza, Palestine.The newborns were divided into four groups based on season of birth: spring (n=72), winter (n=96), autumn (n=103) and summer (n=95). Blood samples were collected in plain vacutainers for assaying bilirubin profile and complete blood count. Bilirubin and complete blood count were assayed by commercial kits. SPSS software was used for data analysis. RESULTS: Indirect and total bilirubin showed significant seasonal variations, whereas direct did not. Spring and winter have increased indirect and total bilirubin. Seasonal hemoglobin levels varied significantly. Red blood cells, hemoglobin, and hematocrit positively correlated with total and indirect bilirubins. CONCLUSION: Spring and winter births exhibited higher indirect and total bilirubin in the first two weeks. The birth season appears to affect newborn jaundice. Short sunshine duration may increase neonatal hyperbilirubinemia risk.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.029
GPT teacher head0.336
Teacher spread0.307 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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