Lymphocyte Transformation Testing for Quantifying Metal-Implant-Related Hypersensitivity Responses
Bibliographic record
Abstract
Hypersensitivity to metallic implants has been documented in case reports and cohort studies. However, this phenomenon remains unpredictable and poorly understood. There is continuing concern about the extrapolation of dermal patch testing to the periimplant environment. The utility of lymphocyte transformation testing (LTT) for predicting implant-related sensitivity in orthopedic patients was evaluated by contrasting LTT and patch-testing protocols and examining original cohort LTT data of subjects with and without implants. LTT of peripheral blood lymphocytes was performed, using four groups: (1) age-matched controls; (2) patients with osteoarthritis (preimplant), with and without dermal metal sensitivity; and (3) patients with total hip arthroplasty. A stimulation index of greater than 2 ( p < .05) indicated metal sensitivity. Patients with osteoarthritis and a history of metal sensitivity were more reactive to nickel than were those of any other group, as expected (ie, 66% incidence and average stimulation index of > 20). However, subjects with implants (group 3) were threefold more reactive to chromium (p < .04) than were controls (group 1) or subjects with osteoarthritis (group 2). Quantifiable lymphocyte reactivity as exemplified by increased incidence and average reactivity levels was metal implant specific (characteristic of adaptive immune responses) and suggests that LTT may be useful in the determination of implant-specific sensitivity. Advantages of LTT include quantitative results and the facilitation of multichallenge agent and dose testing. Thus, LTT (provided by laboratories fully disclosing testing methods) may be an additional tool in the armamentarium of physicians.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".