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Record W4400427682 · doi:10.1038/s44303-024-00026-2

Author Correction: Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

2024· article· en· W4400427682 on OpenAlexaff
Okyaz Eminağa, Fred Saad, Zhe Tian, Ulrich Wolffgang, Pierre I. Karakiewicz, Véronique Ouellet, Féryel Azzi, Tilmann Spieker, Burkhard Helmke, Markus Graefen, Xiaoyi Jiang, Lei Xing, Jörn H. Witt, Dominique Trudel, Sami‐Ramzi Leyh‐Bannurah

Bibliographic record

Venuenpj Imaging · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsProstate cancerMalignancyArtificial intelligenceProstateMedicineComputer scienceCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

In the author’s contribution, the following statement was missing ‘TS evaluated the histological images.’. In the figure caption 2 the title “Kaplan-Meier Survival Analyses.” was missing and for the author “Jorn H. Witt” following affiliation was missing “University Medical Centre Mannheim, Universitätsklinikum Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany”.

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)0.005
metaresearch head score (Gemma)0.125
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: none
Teacher disagreement score0.084
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.125
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0840.036

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 head0.020
GPT teacher head0.343
Teacher spread0.324 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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