Peri‐implant crevicular fluid biomarkers as discriminants of peri‐implant health and disease
Bibliographic record
Abstract
AIM: The objective of this cross-sectional study was to examine the potential of peri-implant crevicular fluid (PICF) analytes to discriminate between peri-implant health and disease using a multi-biomarker approach. METHODS: We collected PICF samples from the mesio-buccal site of every implant (n = 145) from 52 subjects with peri-implantitis and measured the levels of 20 biomarkers using Luminex. We grouped implants and subjects based on the clinical characteristic of the sampled sites and implants into: healthy sites from healthy implants (HH), diseased sites from diseased implants (DD) and healthy sites from diseased implants (HD). The significance of the differences between the HH and DD groups was determined using general linear models controlling for false discovery rate. We used logistic regression to determine the best multi-biomarker models that could distinguish HH from DD subjects and HH from HD subjects. RESULTS: There were statistically significant differences between HH and DD groups for 12/20 biomarkers. Logistic regression resulted in a 6-biomarker model (Flt-3L, GM-CSF, IL-10, sCD40L, IL-17 and TNFα) that discriminated HH from DD subjects (AUC = 0.93) and a 3-biomarker model (IL-17, IL-1ra and vascular endothelial growth factor) that distinguished HH from DD subjects (AUC = 0.90). CONCLUSION: PICF biomarkers might help discriminate peri-implant health from disease.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".