Physician-Patient Agreement in the Assessment of Allergen Relevance
Bibliographic record
Abstract
BACKGROUND: The efficacy of patch testing may be enhanced by data allowing the physician to estimate the likelihood that results of a patch-test reading are relevant to a patient's dermatitis. OBJECTIVE: The goal of this study was to compare the rates of agreement between the physician's assessment of relevance at the time of final reading and the patients' report 3 months to 3 years later in regard to whether avoidance of an allergen was needed to remain free of dermatitis. We hypothesize that the agreement rates between the physician and patient relevance assessments will vary based on properties both intrinsic and extrinsic to the allergen in question. METHODS: We mailed 407 Institutional Review Board-approved questionnaires to patients and analyzed results for the 92 patients reporting greater than 80% improvement of their dermatitis. Cross-reacting allergens tested on the same patient were combined for analysis. Percent agreement was used to assess interrater concordance. RESULTS: Percent agreement regarding relevance for each allergen or group of allergens was as follows: formaldehyde and formaldehyde-releasing preservatives, 88%; neomycin sulfate, 78%; nickel sulfate hexahydrate, 71%; fragrance mix and related products, 65%; and gold sodium thiosulfate, 56%. CONCLUSION: Relevance varies between allergens. Physician assessment of relevance at the time of final reading is not the ideal method for determining allergen relevance. This has implications for when best to determine the relevance of certain allergens. For allergens with lower agreement, in particular, extended follow-up is recommended to accurately determine an allergen's contribution to a patient's allergic contact dermatitis, especially in those circumstances in which a patient's condition has not improved.
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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 | 0.103 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".