Report of the IDEOM Meeting Adjacent to the GRAPPA 2023 Annual Meeting
Bibliographic record
Abstract
The nonprofit organization International Dermatology Outcome Measures (IDEOM) is committed to improving the implementation of patient-centered outcome measures in dermatologic disease. At a conference adjacent to the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2023 annual meeting, the IDEOM Psoriatic Disease Workgroup presented updates on recent efforts in outcome measure advancement. Dr. Alice Gottlieb presented the preliminary findings of a study within the Mount Sinai Health System that aims to determine how well the IDEOM musculoskeletal (MSK) symptom framework, which uses the Psoriasis Epidemiology Screening Tool (PEST) and the Psoriatic Arthritis Impact of Disease (PsAID) instruments, functions in clinical settings. Drs. Joseph Merola and Lourdes Perez-Chada updated attendees on the IDEOM MSK-Q, a 9-item patient-reported questionnaire designed to measure the intensity and impact of MSK symptoms on the quality of life in patients with psoriasis (PsO) with or without psoriatic arthritis (PsA). Dr. Vibeke Strand summarized the Outcome Measures in Rheumatology (OMERACT) 2023 conference sessions. Dr. April Armstrong discussed the preliminary findings of a multicentered study designed to validate the 7-item Dermatology Treatment Satisfaction Instrument (DermSat-7) among patients with PsO. She also introduced the Psoriasis and Psoriatic Arthritis Treatment Satisfaction Instrument, a tool that seeks to capture the level of patient satisfaction with current therapy for PsO and PsA. This report summarizes the developments discussed at the IDEOM PsO and PsA research workgroups during the GRAPPA 2023 annual meeting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.023 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".