Metal hypersensitivity screening among frontline healthcare workers—A descriptive study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The study aims were to (a) assess allergy screening practices, (b) determine the awareness of metal hypersensitivity among frontline healthcare workers and (c) examine perceived barriers to implementing metal hypersensitivity screening into clinical practice. BACKGROUND: Adverse device-related events, such as hypersensitivity to metals, are well documented in the literature. Hypersensitivity to metal is a type IV T-cell-mediated reaction that can occur after cardiac, orthopaedic, dental, gynaecological and neurosurgical procedures where a device with metal components is implanted into the body. Patients with hypersensitivity to metal are likely to experience delayed healing, implant failure and stent restenosis. Identifying patients with a history of metal hypersensitivity reaction could mitigate the risk of poor outcomes following device implant. Yet in clinical practice, healthcare workers do not routinely ask about the history of metal hypersensitivity when documenting allergies. The existing literature does not report why this is not included in allergy assessment. DESIGN: Following the STROBE checklist, a cross-sectional, descriptive study was conducted. METHODS: Frontline healthcare workers were recruited using professional contacts and social online media to complete an online questionnaire. Quantitative data were summarised descriptively while thematic analysis was used to examine barriers to implementation. RESULTS: Three hundred forty-five participants from 14 countries completed the questionnaire, with the majority (187/54%) practicing in Canada, in general medicine and intensive care units. Ninety per cent of the participants did not routinely ask about metal hypersensitivity when evaluating allergy history. Of the respondents, 86% were unaware of the association between metal hypersensitivity and poor patient outcomes. After presented with the evidence, 81% indicated they were likely or very likely to incorporate the evidence into their clinical practice. Common themes about barriers to implementing were 'Standards of Practice', 'Knowledge' and 'Futility of Screening'. CONCLUSION: The findings suggest lack of awareness as the main reason for not including metal in routine allergy assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".