Diagnosis validation and clinical characterization of atopic dermatitis in Nurses’ Health Study 2
Bibliographic record
Abstract
BACKGROUND: Epidemiologic studies of atopic dermatitis (AD) are often limited by case definitions that have not been validated. OBJECTIVE: In this study, we assessed the accuracy of self-report of AD in a large cohort of US female nurses, the Nurses' Health Study 2 (NHS2). We also provide clinical characteristics of AD in the cohort. METHODS: We sent an electronic questionnaire to NHS2 participants who previously reported ever having a diagnosis of AD. This questionnaire was designed to confirm cases of AD using previously validated algorithms with >85% specificity. We assessed the association of AD with asthma, comparing the results when different definitions of AD were applied. We also inquired about various aspects of participants' AD. RESULTS: Responses were received from 2509 of 5126 (49%) nurses who were sent the questionnaire, with an average age of 62. Most participants (1996/2509, 80%) reiterated their previously reported clinician diagnosis of AD. Application of the two diagnostic algorithms yielded confirmation of 1538 and 1293 prevalent cases, respectively. The association of AD with asthma was stronger when more stringent AD case definitions were applied. Participants generally reported mild disease (92% with ≤10% maximal body surface area involved) and a high proportion (57%) reported adult-onset disease. CONCLUSIONS: Self-report of AD diagnosis has good reliability, and future analyses will be strengthened by our ability to conduct sensitivity analyses with refined confirmed AD subgroups.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".