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Record W4406323101 · doi:10.1007/978-1-59259-018-6

Principles of Molecular Rheumatology

2000· book· en· W4406323101 on OpenAlexfundno aff

Bibliographic record

VenueHumana Press eBooks · 2000
Typebook
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
FundersAbramson Family Cancer Research InstituteSchool of Medicine, New York UniversityYork UniversityUniversity of South CarolinaThomas Jefferson UniversityCase Western Reserve UniversityUniversity of WashingtonUniversity of Arkansas for Medical SciencesWake Forest UniversityUniformed Services University of the Health SciencesShriners Hospitals for ChildrenNorthwestern UniversityUniversity of PennsylvaniaUniversity of ConnecticutU.S. Department of Veterans AffairsSyracuse UniversityMcGill UniversityCancer Research Institute
KeywordsRheumatologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

George Tsokos and a panel of authoritative clinicians and researchers synthesize the latest findings from across cell and molecular biology with the basic principles of rheumatology to create the first textbook of molecular rheumatology. These established experts describe the biochemical mechanisms by which apoptosis, cell signaling, complement, lipids, and viruses contribute to disease expression, and detail both immune and nonimmune cell function in rheumatic diseases. Their review of the major rheumatic diseases integrates the cellular, biochemical, and molecular biological mechanisms that are important in rheumatic disease pathogenesis. Path-breaking and illuminating, Principles of Molecular Rheumatology expands the envelope of clinical understanding to reveal the biological roots underlying rheumatologic disease, as well as the nature and roles of the powerful new therapeutics now emerging for its optimal treatment.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0380.037

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 head0.078
GPT teacher head0.321
Teacher spread0.243 · 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
GenreOther

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
Published2000
Admission routes1
Has abstractyes

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