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Record W4251852558 · doi:10.5858/2007-131-805-apiait

Advancing Practice, Instruction, and Innovation Through Informatics (APIII 2006): Scientific Session Presentation Abstracts and Scientific Poster Session Abstracts

2007· article· en· W4251852558 on OpenAlexaboutno aff

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Library sciencePresentation (obstetrics)InformaticsHealth informaticsMedicineScheduleMedical educationManagementPolitical scienceComputer sciencePathologyWorld Wide WebPublic healthSurgeryLaw

Abstract

fetched live from OpenAlex

Abstract Scientific session presentations ( http://apiii.upmc.edu/abstracts/sci_schedule.html ) and scientific poster sessions ( http://apiii.upmc.edu/abstracts/eposter.html ) were conducted at the 11th annual international conference on Advancing Practice, Instruction, and Innovation Through Informatics (APIII 2006) on August 15–18, 2006, at the Sheraton Vancouver Wall Centre, located in Vancouver, British Columbia, Canada. One of the course directors was Michael J. Becich, MD, PhD, professor of pathology and information sciences and telecommunications, chairman of the Department of Biomedical Informatics at the University of Pittsburgh, Pittsburgh, Pa. Also serving as course directors were John R. Gilbertson, MD, director of Pathology Informatics, Case Western University, Cleveland, Ohio; Walter Henricks, MD, director of Pathology Informatics, The Cleveland Clinic Foundation, Cleveland, Ohio; and Bruce McManus, MD, PhD, professor and codirector, The iCAPTURE Centre, University of British Columbia–St Paul's Hospital, Vancouver, Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2007
Admission routes1
Has abstractyes

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