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Record W4394600887 · doi:10.1212/wnl.0000000000205000

Randomized Controlled Trial of a Smartphone-based Preventive Migraine Self-management Program in the Emergency Department Setting: A Promising Teachable Moment (S22.004)

2024· article· en· W4394600887 on OpenAlexaff
Mia T. Minen, Elizabeth K. Seng, Benjamin W. Friedman, Alexis George, Kristina M. Fanning, Ryan Bostic, Scott W. Powers, Richard B. Lipton

Bibliographic record

VenueNeurology · 2024
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsTeachable momentMigraineMedicineRandomized controlled trialEmergency departmentPhysical therapyRelaxation (psychology)Medical emergencyPhysical medicine and rehabilitationPsychologyAnesthesiaSurgeryPsychiatryInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

We examined whether a smartphone self-management progressive muscle relaxation (PMR) based therapy improved patient-centered outcomes for migraine compared to enhanced usual care (EUC).

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)0.001
metaresearch head score (Gemma)0.002
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: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.001

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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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
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 abstractyes

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