The lived experiences of metal hypersensitivity
Bibliographic record
Abstract
Background Immunological responses to metal ions can occur in people with metal implantable devices. Nevertheless, patients are not routinely asked about metal hypersensitivity nor tested for it prior to surgical procedures. Moreover, we have not identified any published literature on patients’ experiences with this condition. We undertook this study to better understand the experiences of patients with metal hypersensitivity and to determine what information would help them make an informed decision about implantable medical devices with metal compositions. Methods This is a patient-oriented research using an interpretative phenomenological methodology. We enrolled 16 people from five countries (Canada, New Zealand, Spain, the UK, and the USA) who experienced metal hypersensitivity following implantable medical device. We collected data from in-depth, semi-structured interviews focused on pre-procedural patient education and postoperative experiences to elucidate the barriers and opportunities related to metal hypersensitivity. We analyzed the data using interpretative phenomenological analysis. Results The majority were white biological women between the ages of 34 and 65. None of the participants were informed about immunological responses to metals in implants during the consenting process. They encountered many challenges when symptoms of metal hypersensitivity occurred. The outcomes for the few who had the implants removed were mixed. Conclusion Hypersensitivity reactions to metal need to be discussed as part of the consenting process, and additional strategies are needed to mitigate this issue in the health system.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".