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Record W4390898294 · doi:10.2196/55928

Correction: A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

2024· erratum· en· W4390898294 on OpenAlexvenueno aff
Marianna Masiero, Gea Elena Spada, Virginia Sanchini, Elisabetta Munzone, Ricardo Pietrobon, Lucas Teixeira, Mirtha Amanda Angulo‐Valencia, Aline Machiavelli, Elisa Fragale, Massimo Pezzolato, Gabriella Pravettoni

Bibliographic record

VenueJMIR Research Protocols · 2024
Typeerratum
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersPfizer
KeywordsRandomized controlled trialProtocol (science)Decision support systemMedicineBreast cancerMetastatic breast cancerCancerComputer sciencePhysical therapyMedical physicsArtificial intelligenceAlternative medicineOncologyInternal medicinePathology

Abstract

fetched live from OpenAlex

[This corrects the article DOI: 10.2196/48852.].

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)0.029
metaresearch head score (Gemma)0.330
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.193
Threshold uncertainty score0.647

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.330
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.1930.026

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 head0.124
GPT teacher head0.498
Teacher spread0.374 · 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 designRandomized trial
Domainnot available
GenreProtocol

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
Published2024
Admission routes1
Has abstractyes

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