Orthopedic Prostheses: Is There Any Point in Patch Testing?
Bibliographic record
Abstract
The aging of our population and the availability of new medical technologies have contributed to tremendous growth in the number of patients with implanted orthopedic and other devices. As experts on contact dermatitis, we are periodically asked to evaluate patients who have a history of metal allergy prior to surgery. We are sometimes faced with patients who have experienced complications after implant surgery or (more rarely) who have developed skin disease temporally related to surgery. To address these controversial issues, we asked three experts to discuss the role of patch testing in regard to patients with orthopedic implants. Mark D.P. Davis, MD, is an Associate Professor in the Department of Dermatology, Mayo Clinic, Rochester, MN; Christen M. Mowad, MD, is Assistant Professor of Dermatology, Geisinger Medical Center, Danville, PA; and Pamela Scheinman, MD, is Director of the Contact Dermatitis and Occupational Dermatology Unit and Assistant Professor of Dermatology, Tufts-New England Medical Center, Boston, MA.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".