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Record W2168886412 · doi:10.1038/clpt.2014.150

Inflammation: The Dynamic Force of Health and Disease

2014· editorial· en· W2168886412 on OpenAlexaff
Vessela Vassileva, Micheline Piquette‐Miller

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2014
Typeeditorial
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInflammationClinical pharmacologyDiseaseMedicineDrug developmentHappinessBalance (ability)Immune systemDrugClinical diseaseIntensive care medicineImmunologyPharmacologyPsychologyInternal medicinePhysical therapyPsychotherapist

Abstract

fetched live from OpenAlex

Replacing "happiness" with "inflammation" in Thomas Merton's quotation holds true for the processes that govern our immune response and health. The balance between pro- and anti-inflammatory signals regulates inflammatory responses, leading to either restoration of health or the development and progression of disease, depending on whether it creates equilibrium or dysfunction. This issue of Clinical Pharmacology & Therapeutics highlights emerging research and concepts related to inflammation and its underlying role in chronic disease and variable drug response.

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)0.006
metaresearch head score (Gemma)0.018
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.002
Science and technology studies0.0030.006
Scholarly communication0.0080.007
Open science0.0030.002
Research integrity0.0100.026
Insufficient payload (model declined to judge)0.0050.005

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 head0.040
GPT teacher head0.419
Teacher spread0.378 · 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
GenreEditorial

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

Citations10
Published2014
Admission routes1
Has abstractyes

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