Cutaneous and Systemic Hypersensitivity Reactions to Metallic Implants
Bibliographic record
Abstract
Cutaneous reactions to metal implants, orthopedic or otherwise, are well documented in the literature. The first case of a dermatitis reaction over a stainless steel fracture plate was described in 1966. Most skin reactions are eczematous and allergic in nature, although urticarial, bullous, and vasculitic eruptions may occur. Also, more complex immune reactions may develop around the implants, resulting in pain, inflammation, and loosening. Nickel, cobalt, and chromium are the three most common metals that elicit both cutaneous and extracutaneous allergic reactions from chronic internal exposure. However, other metal ions as well as bone cement components can cause such hypersensitivity reactions. To complicate things, patients may also develop delayed-type hypersensitivity reactions to metals (ie, in-stent restenosis, prosthesis loosening, inflammation, pain, or allergic contact dermatitis) following the insertion of intravascular stents, dental implants, cardiac pacemakers, or implanted gynecologic devices. Despite repeated attempts by researchers and clinicians to further understand this difficult area of medicine, the association between metal sensitivity and cutaneous allergic reactions remains to be fully understood. This review provides an update of the current knowledge in this field and should be valuable to health care providers who manage patients with conditions related to this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".