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A Meta-Analysis of Sensitive Periods for the Effects of Childhood Adversity on DNA Methylation

2024· article· en· W4396237040 on OpenAlexaff
Alexandre A. Lussier, Natasha Wood, Jonah Fischer, Mannan Luo, Phillip E. Melton, Alexander Neumann, Johanna Tuhkanen, Erin B. Ware, Charlotte A. M. Cecil, Sarah Cohen‐Woods, Janine F. Felix, Rae‐Chi Huang, Marie‐France Hivert, Kalsea J. Koss, Jari Lahti, Michael J. Meaney, Helen C.S. Meier, Colter Mitchell, Daniel A. Notterman, Kieran J. O’Donnell, Katri Räikkönen, Caroline L. Relton, Sara Sammallahti, Lisa Schneper, Kerry J. Ressler, Andrew J. Simpkin, Matthew Suderman, Esther Walton, Andrew Smith, Erin C. Dunn

Bibliographic record

VenueBiological Psychiatry · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPsychosocial Factors Impacting Youth
Canadian institutionsMcGill University
Fundersnot available
KeywordsDNA methylationMeta-analysisPsychologyMethylationGeneticsDevelopmental psychologyDNABiologyMedicineInternal medicineGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.017
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.036
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.051
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
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 head0.094
GPT teacher head0.369
Teacher spread0.275 · 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 designMeta-analysis
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

Citations0
Published2024
Admission routes1
Has abstractno

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