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Record W3006058568 · doi:10.2196/16932

A Physician-Completed Digital Tool for Evaluating Disease Progression (Multiple Sclerosis Progression Discussion Tool): Validation Study

2020· article· en· W3006058568 on OpenAlexaff
Tjalf Ziemssen, Daniela Piani-Meier, Bryan Bennett, Chloe Johnson, Katie Tinsley, Andrew Trigg, Thomas Hach, Frank Dahlke, Davorka Tomic, Chloe Tolley, Mark S. Freedman

Bibliographic record

VenueJournal of Medical Internet Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Ottawa
FundersNovartis Pharma
KeywordsMedicineReceiver operating characteristicMultiple sclerosisExpanded Disability Status ScaleArea under the curveInternal medicinePhysical therapyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Defining the transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive multiple sclerosis (SPMS) can be challenging and delayed. A digital tool (MSProDiscuss) was developed to facilitate physician-patient discussion in evaluating early, subtle signs of multiple sclerosis (MS) disease progression representing this transition. OBJECTIVE: This study aimed to determine cut-off values and corresponding sensitivity and specificity for predefined scoring algorithms, with or without including Expanded Disability Status Scale (EDSS) scores, to differentiate between RRMS and SPMS patients and to evaluate psychometric properties. METHODS: Experienced neurologists completed the tool for patients with confirmed RRMS or SPMS and those suspected to be transitioning to SPMS. In addition to age and EDSS score, each patient's current disease status (disease activity, symptoms, and its impacts on daily life) was collected while completing the draft tool. Receiver operating characteristic (ROC) curves determined optimal cut-off values (sensitivity and specificity) for the classification of RRMS and SPMS. RESULTS: Twenty neurologists completed the draft tool for 198 patients. Mean scores for patients with RRMS (n=89), transitioning to SPMS (n=47), and SPMS (n=62) were 38.1 (SD 12.5), 55.2 (SD 11.1), and 69.6 (SD 12.0), respectively (P<.001, each between-groups comparison). Area under the ROC curve (AUC) including and excluding EDSS were for RRMS (including) AUC 0.91, 95% CI 0.87-0.95, RRMS (excluding) AUC 0.88, 95% CI 0.84-0.93, SPMS (including) AUC 0.91, 95% CI 0.86-0.95, and SPMS (excluding) AUC 0.86, 95% CI 0.81-0.91. In the algorithm with EDSS, the optimal cut-off values were ≤51.6 for RRMS patients (sensitivity=0.83; specificity=0.82) and ≥58.9 for SPMS patients (sensitivity=0.82; specificity=0.84). The optimal cut-offs without EDSS were ≤46.3 and ≥57.8 and resulted in similar high sensitivity and specificity (0.76-0.86). The draft tool showed excellent interrater reliability (intraclass correlation coefficient=.95). CONCLUSIONS: The MSProDiscuss tool differentiated RRMS patients from SPMS patients with high sensitivity and specificity. In clinical practice, it may be a useful tool to evaluate early, subtle signs of MS disease progression indicating the evolution of RRMS to SPMS. MSProDiscuss will help assess the current level of progression in an individual patient and facilitate a more informed physician-patient discussion.

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)0.011
metaresearch head score (Gemma)0.022
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 score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.301
GPT teacher head0.503
Teacher spread0.201 · 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".

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Citations54
Published2020
Admission routes1
Has abstractyes

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