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Record W4412175184 · doi:10.6000/1929-6029.2025.14.33

Association between Body Mass Index and Complete Blood Count Parameters

2025· article· en· W4412175184 on OpenAlexvenueno aff
Ghaidaa Rifaat Hamid, Rayan Zaidan Khalaf

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2025
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsBody mass indexIndex (typography)Association (psychology)StatisticsMathematicsMedicineInternal medicineComputer sciencePsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Obesity is an abnormal or extraordinary cumulative fat in the body which can lead to health problems. Its prevalence increased worldwide. This study aims to identify the effect of obesity on CBC parameters in obese subjects. Two hundred obese patients of either gender, the mean age was (33.71±2) years, 148 were females (74.4%) and 52 were males (25.6%), each subject submitted to medical history, physical examination, and CBC test. The results of the study revealed that there was a correlation between BMI and CBC parameters, (P value <0.05). This study concluded that there is positive correlation between CBC and BMI (positively correlated, p-value <0.05).

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)0.000
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.413
Teacher spread0.371 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueInternational Journal of Statistics in Medical ResearchSame topicAdipokines, Inflammation, and Metabolic DiseasesFrench-language works237,207