Metal Hypersensitivity and Orthopedic Implants: Survey of Orthopedic Surgeons
Bibliographic record
Abstract
BACKGROUND: There is no clear consensus among orthopedic surgeons concerning metal hypersensitivity screening and orthopedic implants. OBJECTIVE: This study investigated practices and opinions about metal hypersensitivity and orthopedic implants via a survey administered to practicing orthopedists. METHODS: A questionnaire was sent to members of the Pennsylvania Orthopaedic Society electronically. Respondents were asked about preoperative and postoperative screening habits concerning metal hypersensitivity and implants. RESULTS: Forty-four physicians completed the survey. Only 11% of respondents reported that they always or often screen patients for metal hypersensitivity. Fifty percent of respondents stated that they only rarely refer patients for patch testing (and the remainder never do). If, however, patients were found to have a positive patch test, most providers were very likely to use a different implant. Other respondents were skeptical of the relationship between metal hypersensitivity and implant failure. Dermatitis, pain, and loosening were common reasons for postoperative testing. Seventy percent of respondents said that patch testing rarely or never changed their decision making. CONCLUSIONS: This study is reflective of the lack of consensus between orthopedists regarding patch testing. It demonstrates the diversity of opinions among orthopedists, the need for additional dialogue between orthopedic and dermatology specialties, and the need for larger studies investigating outcomes and metal hypersensitivity.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".