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Record W4308634188 · doi:10.1111/odi.14430

Oral lichen planus clinician reported outcome measure: Development, content validity, and further development

2022· article· en· W4308634188 on OpenAlexaff
Michael T. Brennan, Richeal Ní Ríordáin, Leslie Long‐Simpson, Caroline Bissonnette, Marcela Lizano, Lars Siim Madsen

Bibliographic record

VenueOral Diseases · 2022
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversité de Montréal
FundersNational Institute for Health and Care Research
KeywordsOral lichen planusMedicineInternal medicineClinical PracticeGastroenterologyClinical trialDermatologyPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To establish and test a clinician-reported outcome measure of oral lichen planus (OLP): OLP Investigator global assessment (IGA). METHODS: OLP IGA scale was tested with retrospective data from clinical practice and a phase II clinical trial. A comparison of the OLP IGA score with patient-reported outcomes was completed. RESULTS: Clinical Practice: The mean (SD) OLP IGA score (0-4) in 107 OLP patients was 1.8 (1.0) with correlation of 0.25-0.48 (p value 0.01 - <0.0001) with symptom scores. There was a significant increase in OLP symptoms based on OLP IGA score. CLINICAL TRIAL: The mean (SD) OLP IGA score in 137 research participants was 2.5 (1.2) with correlation of 0.43-0.52 (all p values <0.0001) with symptoms scores. There was a significant increase in OLP symptoms based on OLP IGA score. Forty-seven (35%) participants in the phase 2 study had an improvement in the OLP IGA score of ≥2. There were significant improvements in all symptoms scores in relation to the change in IGA score. CONCLUSIONS: The OLP IGA is designed to assess changes in symptomatic OLP lesions and is appropriate for use across the full range of symptomatic OLP severity and represents a scale with utility in clinical practice and clinical trials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.339
GPT teacher head0.386
Teacher spread0.047 · 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 teacher head, not a consensus.

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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