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Record W4386190968 · doi:10.31219/osf.io/vahdw

Mycotoxins and Bone Growth: A review of the literature on associations between Xenobiotic Exposure and Bone Growth and Development

2023· review· en· W4386190968 on OpenAlexaff
Rukshan Mehta, Anthony Wenndt

Bibliographic record

Venuenot available
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMycotoxinBiologyBone developmentMicronutrientAflatoxinLinear growthToxinPhysiologyMedicineBiotechnologyEndocrinologyPathologyMicrobiology

Abstract

fetched live from OpenAlex

Mycotoxins are secondary metabolites of fungi that are known and hypothesized to be associated with linear growth faltering because of their impact on inflammation, intestinal damage, inhibition of protein synthesis and micronutrient absorption. In this narrative review, we aim to extend this analysis to further explore associations between mycotoxins (Aflatoxins, Ochratoxins, Trichothecenes including Deoxynivalenol, T-2 Toxin and Fumonisins) and long-bone growth, particularly during the saltatory periods of development. Given that linear growth is a direct function of skeletal development and long-bone growth, we explore biological pathways and mechanisms of impact of these toxins in both animal and human studies, in addition to the epidemiology literature (post 2020). Based on preliminary findings from this review, we have identified a need for further research and examination of how these toxins and exposures may be studied in humans to elucidate the downstream impact on bone related biomarkers and anthropometric indices used to identify and predict stunting in population-based studies.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.263
Teacher spread0.217 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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