Bibliographic record
Abstract
BACKGROUND: Cutaneous metal hypersensitivity reactions (MHR) are common but rare with implanted devices. OBJECTIVES: This study aimed to characterize the opinions of dermatologists who are actively evaluating/advising patients with MHR. METHODS: A questionnaire was distributed to all individuals who attended the European Society of Contact Dermatitis (ESCD) 2012 and the American Contact Dermatitis Society 2013 meetings. RESULTS: A total of 119 individuals responded with a participation rates of 10% (ESCD) and 32% (American Contact Dermatitis Society). Ninety-six percent of the respondents evaluate MHR and 91% were attending physicians. Orthopedic and dental devices were common problems compared with cardiovascular devices. Patch testing is the top choice for evaluating MHR. Lymphocyte transformation and intradermal tests are rarely used. Eighty-two percent of the respondents evaluate plastic/glue components in symptomatic patients postimplant. Most dermatologists use a tray specifically for joint allergy or a history-based custom array of allergens. Those patients with a strong clinical history of metal allergy should be evaluated before metal implantation (54%), whereas others forgo evaluation and recommend a titanium implant based on history alone (38%). Diagnostic criteria for postimplant reactions were evaluated. Eight percent of the respondents felt that no evaluation was necessary, with ESCD respondents being significantly more likely to not recommend evaluation (P = 0.001). CONCLUSIONS: Metal hypersensitivity reactions consultation requests are common for preimplant and postimplant issues. Patch testing is currently the best test for MHR.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".