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Record W2984569157 · doi:10.1016/j.jaac.2019.10.011

Meta-analysis: Exposure to Early Life Stress and Risk for Depression in Childhood and Adolescence

2019· review· en· W2984569157 on OpenAlexafffund
Joelle LeMoult, Kathryn L. Humphreys, Alison Tracy, Jennifer-Ashley Hoffmeister, Eunice Ip, Ian H. Gotlib

Bibliographic record

VenueJournal of the American Academy of Child & Adolescent Psychiatry · 2019
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research CouncilNational Institute of Mental HealthNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchStanford Maternal and Child Health Research InstituteBrain and Behavior Research FoundationVanderbilt Kennedy Center, Vanderbilt University Medical CenterJacobs FoundationNational Institutes of HealthMichael Smith Health Research BC
KeywordsMajor depressive disorderDepression (economics)Meta-analysisOdds ratioSexual abusePhysical abuseMedicineClinical psychologyPsychologyPsychiatryDemographyPoison controlInjury preventionInternal medicineEnvironmental healthMood

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)0.007
metaresearch head score (Gemma)0.022
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: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.033
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.043
GPT teacher head0.335
Teacher spread0.292 · 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
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

Citations639
Published2019
Admission routes2
Has abstractno

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