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Record W4391166569 · doi:10.2196/56570

Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence–Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study

2024· erratum· en· W4391166569 on OpenAlexaffvenue
Christina Popescu, Grace Golden, David Benrimoh, Myriam Tanguay-Sela, Dominique Slowey, Eryn Lundrigan, Jerome D. Williams, Bennet Desormeau, Divyesh Kardani, Tamara Pérez, Colleen Rollins, Sonia Israel, Kelly Perlman, Caitrin Armstrong, Jacob Baxter, Kate Whitmore, Marie-Jeanne Fradette, Kaelan Felcarek-Hope, Ghassen Soufi, Robert Fratila, Joseph Mehltretter, Karl Looper, Warren Steiner, Soham Rej, Jordan F. Karp, Katherine Heller, Sagar V. Parikh, Rebecca McGuire-Snieckus, Manuela Ferrari, Howard C. Margolese, Gustavo Turecki

Bibliographic record

VenueJMIR Formative Research · 2024
Typeerratum
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsDouglas Mental Health University InstituteMcGill UniversityUniversity of Waterloo
Fundersnot available
KeywordsDepression (economics)Longitudinal studyWeb applicationPsychologyDecision support systemComputer scienceMedical physicsArtificial intelligenceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

[This corrects the article DOI: 10.2196/31862.].

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)0.010
metaresearch head score (Gemma)0.179
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.179
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.003
Science and technology studies0.0050.004
Scholarly communication0.0050.003
Open science0.0050.002
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0570.029

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 head0.471
GPT teacher head0.649
Teacher spread0.178 · 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
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 routes2
Has abstractyes

Explore more

Same venueJMIR Formative Research→Same topicDigital Mental Health Interventions→French-language works237,207→