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Record W4391321981 · doi:10.1002/ajhb.24042

Hypoxia, <scp>NSAIDs</scp>, and autism: A biocultural analysis of stressors in gametogenesis

2024· review· en· W4391321981 on OpenAlexaff
Stacie Burke

Bibliographic record

VenueAmerican Journal of Human Biology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInflammationGametogenesisStressorHypoxia (environmental)FolliculogenesisAutismBiologyEpigenomeMedicineImmunologyPsychologyNeuroscienceDevelopmental psychologyGeneticsGene

Abstract

fetched live from OpenAlex

Cultural and generational trends have increasingly favored "anti-inflammatory" action, innovating a new class of analgesic, non-steroidal anti-inflammatory drugs (NSAIDs) in the 20th century. The modern human body has been molded over evolutionary time and while acknowledging inflammation can be pathologically entwined, it also serves an important role in healthy folliculogenesis and ovulation, shaping cues that drive needed vascular change. This review argues that because of anti-inflammatory action, the cultural invention of NSAIDs represents a particular stressor on female reproductive-age bodies, interacting with natural, underlying variation and placing limits on healthy growth and development in the follicles, creating potential autism risk through hypoxia and mutagenic or epigenetic effects. Since testes are analogs to ovaries, the biological grounding extends naturally to spermatogenesis. This review suggests the introduction of over-the-counter NSAIDs in the 1980s failed to recognize the unique functioning of reproductive-age bodies, challenging the cyclical inflammation needed for healthy gamete development. NSAIDs are framed as one (notable) stressor in an anti-inflammatory era focused on taming the risks of inflammation in modern human life.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.335
Teacher spread0.302 · 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.

Study designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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