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Record W2072589231 · doi:10.1503/cmaj.051611

Assessing and scaling the knowledge pyramid: the good-guideline guide

2006· article· en· W2072589231 on OpenAlexafffundvenue
Dave Davis

Bibliographic record

VenueCanadian Medical Association Journal · 2006
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMinistry of Health and Long Term CareOntario Medical Association
FundersUniversity of Toronto
KeywordsPyramid (geometry)GuidelineComputer scienceKnowledge basePrimary careHealth professionalsData scienceMedical knowledgeHealth careKnowledge managementMedical educationMedicineWorld Wide WebFamily medicinePathology

Abstract

fetched live from OpenAlex

Physicians and other health care professionals are increasingly exposed to an overwhelming amount of medical literature. Thousands of primary research studies form the base of an enormous pyramid of knowledge, unscalable by the individual clinician, however industrious, critical and self-directed.[1

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)0.054
metaresearch head score (Gemma)0.131
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.131
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0130.007
Science and technology studies0.0030.005
Scholarly communication0.0090.009
Open science0.0080.006
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0060.005

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 head0.207
GPT teacher head0.524
Teacher spread0.317 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations8
Published2006
Admission routes3
Has abstractyes

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