Titanium Allergy: A Retrospective Review of 166 Patch Tested Patients
Bibliographic record
Abstract
Abstract: Background: Although most patients do not develop hypersensitivity reactions to metals in implanted devices, when they do occur, significant morbidity can result. Titanium-based systems are often considered as an alternative option in metal-allergic patients; however, there are few studies published on titanium allergy and allergic reactions to titanium in implants may be overlooked in clinical practice. Methods: Our aim was to further characterize a single institution's experience with titanium patch testing and evaluation of titanium allergy. We performed a retrospective medical record review of 166 patients evaluated for titanium contact allergy between January 2018-August 2023. Of the 166 patients in our cohort, 67 were referred for pre-implant patch testing and 64 for post-implant patch testing; 35 were tested for reasons unrelated to an implant. Results: Twenty-six of the 166 patients were PTP to titanium (15.7% positive rate). Titanium PTP rates were higher for post-implant cases (28.1%, 18/64) compared to pre-implant cases (6.0%, 4/67) (χ 2 9.97, p = 0.002). Among 18 titanium PTPs identified for the 64 post-implant cases, 8 were likely relevant, 8 possibly relevant, and 2 not likely relevant. Conclusions: Further studies should be performed to evaluate the incidence of allergy to titanium implants and to continue surveillance of changes in sensitization rates.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".