Clinical, radiographic, and biochemical evaluation of two‐piece versus one‐piece single implants with a laser‐microgrooved collar surface after 5 years of functional loading
Bibliographic record
Abstract
AIM: To compare the clinical and radiographic conditions and the expression of pro-inflammatory cytokines in peri-implant crevicular fluid (PICF) at two-piece/bone level (TP/BL) versus one-piece/tissue level (OP/TL) single implants with a laser-microgrooved collar after at least 5 years of loading. MATERIALS AND METHODS: In total, 20 single TP/BL implants and 20 contralateral OP/TL implants, both with a laser-microgrooved collar surface, in 20 systemically and periodontally healthy subjects (12 males and 8 females, between the age of 36 and 64 [mean age of 49.7 ± 12.3 years]), were examined. Levels of IL-1β, IL-1RA, IL-6, IL-8, IL-17, b-FGF, G-CSF, GM-CSF, IFN, MIP-1β, TNF-α, and VEGF were assessed in PICF using the Bio-Plex 200 Suspension Array System. Plaque index (PI), probing depth (PD), bleeding on probing (BOP), and gingival recession (REC) were recorded. Radiographic crestal bone levels (CBL) were assessed at the mesial and distal aspects of the implant sites. RESULTS: The mean PI, PD, BOP, and REC values had no significant differences in either group. A higher mean value of CBL with statistical difference was detected for TP/BL compared with OP/TL implants. The levels of IL-1β, IL-6, IL-8, GM-CSF, and MIP-1β and TNF-α were higher at TP/BL implants than at OP/TL implants. However, only IL-1β, IL-6, and TNF-α values presented significant differences between the groups. CONCLUSIONS: Although after 5 years of loading single TP/BL and OP/TL implants with a laser-microgrooved collar surface presented similar good clinical conditions, a higher proinflammatory state and higher crestal bone loss were detected for TP/BL implants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".