International Dermatology Outcome Measures (IDEOM): Report from the 2020 Annual Meeting
Bibliographic record
Abstract
BACKGROUND: The International Dermatology Outcome Measures (IDEOM) initiative is a non-profit organization that aims to develop evidence-based outcome measurements to evaluate the impact of treatments for patients with dermatological disease. IDEOM includes all key stakeholders in dermatology (patient, physician, industry, insurer, and government) during the process of developing such outcome measurements. SUMMARY: Here, we provide an update of IDEOM activities that were presented at the 2020 IDEOM Virtual Annual Meeting (October 23-24, 2020). During the meeting, multiple IDEOM workgroups (psoriasis, psoriatic arthritis, hidradenitis suppurativa, acne, pyoderma gangrenosum, and actinic keratosis) shared their progress to date, as well as future directions in developing and validating Patient-Reported Outcome Measures. Updates on demonstrating efficacy in clinicals trials by the US Food and Drug Administration are also summarized. KEY MESSAGES: In this report, we summarize the work presented by each IDEOM workgroup (psoriasis, psoriatic arthritis, hidradenitis suppurativa, acne, pyoderma gangrenosum, and actinic keratosis) at the 2020 IDEOM Virtual 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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".