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Record W4404731394 · doi:10.1016/j.rhum.2024.10.446

L’intelligence articficielle prédit efficacement le focus score et la maladie de Sjögren en identifiant les pattern histologiques associés à cette prédiction

2024· article· fr· W4404731394 on OpenAlexaff
Julien Duquesne, Charlotte Claye, Louis Basseto, Michael Barnes, Elena Pontarini, Stefano Bombardieri, Amaya Gallagher-Syed, Burkhard Fischer, Saba Nayar, Athanasios G. Tzioufas, Andreas V. Goules, L. Chatzis, Wan‐Fai Ng, B. Scane, Mary Anne Medeiros Bandeira, Vasco C. Romão, G. Nocturne, T. Lazure, Clovis Adam, X. Mariette, Vincent Bouget, Samuel Bitoun

Bibliographic record

VenueRevue du Rhumatisme · 2024
Typearticle
Languagefr
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicineDictionGynecologyPhilosophyPoetry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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 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

Citations0
Published2024
Admission routes1
Has abstractno

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