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Record W4224074785 · doi:10.1111/1541-4337.12943

Camel milk composition by breed, season, publication year, and country: A global systematic review, meta‐analysis, and meta‐regression

2022· review· en· W4224074785 on OpenAlexaff
Omar A. Alhaj, Noor Altooq, Ahmad F. Alenezi, Abdulrahman Janahi, Mohamed I. Janahi, Ali Humood, Maha Al‐Rasheed, Nicola Luigi Bragazzi, Haitham Jahrami, Bernard Faye

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsYork University
Fundersnot available
KeywordsMeta-regressionMeta-analysisBreedComposition (language)BiologyGeographyAnimal scienceMedicineInternal medicineArtLiterature

Abstract

fetched live from OpenAlex

Abstract Camel milk consists of an essential macro/micronutrient for human nutrition in the arid and urban regions. This review study aimed to use meta‐analysis statistical techniques for assessment and correction of publication bias, exploration of heterogeneity between studies, and detailed assessment of the effect of a comprehensive set of moderators including breed, season, country, year of publication, and the interaction between composition elements. This could provide a single synthesis of the camel milk composition to warrant strong generalizability of results, examine variability between available studies, and analyze differences in camel milk composition among different exposures. Such a finding will aid future researchers and health professionals in acquiring a more precise understanding of camel milk composition and drawing more clinical implications. Six searching databases and bibliographic were used including PubMed/MEDLINE, ScienceDirect, Springer, EBSCOhost, Scopus, and Web of Science from January 1980 to December 2021. The DerSimonian–Laird estimator was used to create the current random‐effects meta‐analysis. This systematic review and meta‐analysis included a total of 7298 camel milk samples from 23 countries. This review comprises 79 studies published in the English language on or after 1980, including a subgroup of 117 analyses consisting of seasons, sub‐breeds, and countries. The contents of macro/micronutrients in camel milk were identified as follows: protein, 3.17%; fat, 3.47%; lactose, 4.28%; ash, 0.78%; and total solids, 11.31%; calcium, 112.93 mg/100 g; iron, 0.45 mg/100 g; potassium, 116.13 mg/100 g; magnesium, 9.65 mg/100 g; sodium, 53.10 mg/100 g; zinc, 1.68 mg/100 g; vitamin C, 5.38 mg/100 g; vitamin A, 0.36 mg/100 g; vitamin B 1 ,0.05 mg/100 g; vitamin B 2 , 0.13 mg/100 g; vitamin B 3 , 0.51 mg/100 g; vitamin B 6 , 0.09 mg/100 g; and vitamin B 12 , 0.0039 mg/100 g. Our meta‐regression analysis found that fat and total solids were statistically significant moderators of protein; moreover, total solids content is a statistically significant moderator of fat. Discrepancies observed in camel milk profiles are dependent upon several factors, including number of included studies, number of samples, different analytical techniques, feeding patterns, camel's breeds, geographical locations, and seasonal variations.

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)0.021
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.049
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.147
GPT teacher head0.342
Teacher spread0.195 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations37
Published2022
Admission routes1
Has abstractyes

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