Allergic reaction of poly‐ether‐ether‐ketone versus titanium implants: A posttest‐only control group design experimental study using a rabbit model
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine clinically and genetically the allergic effects of titanium and poly-ether-ether-ketone (PEEK) implants following loading in rabbit tibias. MATERIALS AND METHODS: This study included 18 white New Zealand male rabbits (n = 18) divided evenly into three groups: control, titanium (Ti), and PEEK (P). Clinically, the allergenic effect of titanium and PEEK was investigated by detecting the effect on lymph nodes. Furthermore, RT-PCR and ELISA were used to detect the expression of certain genes IL-6, TNF-α, OPG, RANKL, and RUNX-2 through both types of implants. RESULTS: Our findings demonstrated that titanium implants induced enlarged lymph nodes, which PEEK did not. Overall, RT-PCR and ELISA techniques revealed that Ti implants had higher expression of the inflammatory genes IL-6 and TNF-α. Ti had the highest expression in OPG findings, while PEEK had the lowest. RANKL expression was highest in the control group and lowest in the PEEK group. RUNX-2 is the highest for the control group and the lowest for the titanium group. CONCLUSION: Although titanium implants elicited greater allergy responses than PEEK implants, titanium has the highest expression of bone formation genes and the lowest expression of bone resorption genes, making it preferable to PEEK.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".