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Turning Up the Heat

2006· article· en· W2092153909 on OpenAlexaff
Gregory E. Miller, Ekin Blackwell

Bibliographic record

VenueCurrent Directions in Psychological Science · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStressorInflammationDiseaseDepression (economics)PsychologyImmune systemDepressive symptomsNeuroscienceMedicineInternal medicineImmunologyCognition

Abstract

fetched live from OpenAlex

Mounting evidence indicates that chronic stressors and depressive symptoms contribute to morbidity and mortality from cardiac disease. However, little is known about the underlying mechanisms responsible for these effects or about why depressive symptoms and cardiac disease co-occur so frequently. In this article we outline a novel model that seeks to address these issues. It asserts that chronic stressors activate the immune system in a way that leads to persistent inflammation. With long-term exposure to the products of inflammation, people develop symptoms of depression and experience progression of atherosclerosis, the pathologic condition that underlies cardiac disease.

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)0.002
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0410.013

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 head0.068
GPT teacher head0.477
Teacher spread0.409 · 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
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

Citations169
Published2006
Admission routes1
Has abstractyes

Explore more

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