Allergic Sensitization to Nickel and Implanted Metal Devices: A Perspective
Bibliographic record
Abstract
ABSTRACT: There is continuing interest in the interrelationships between allergic sensitization to metal allergens, metal implants, and the development of adverse reactions to implanted devices. Here, we focus on sensitization to nickel (although, in practice, it is commonly not possible to distinguish between events associated with nickel and other potentially allergenic metals used in devices). The purpose of this article was to review whether exposure to nickel resulting from implanted devices is associated with the development of de novo sensitization to nickel and also whether nickel sensitization, either newly acquired or pre-existing, has a causal relationship with adverse health effects. In addressing these issues, a variety of devices, including metal-on-metal hip implants, cardiac and endovascular stents and filters, and the gynecologic implant Essure, are considered. Also addressed is the question of whether pre-operative assessment of nickel allergy (and allergy to other implant metals) is required. The conclusions reached are that (a) sensitization can potentially be acquired as the result of exposure to implants containing nickel, but is not a common occurrence; (b) sensitization to nickel and/or other metal allergens is very rarely a cause of adverse reactions to implants; and (c) routine preoperative patch testing for sensitization to nickel is unnecessary, unless there is a significant clinical history of nickel allergy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".