A Clinical, Radiographic, and Microbiologic Comparison of Astra Tech and Brånemark Single Tooth Implants
Bibliographic record
Abstract
BACKGROUND: The soft tissues around single tooth implants differ fundamentally from the gingiva around natural teeth. There are very limited data comparing soft tissues around different implant systems. AIM: To assess whether the design characteristics of dental implants, particularly the implant-abutment junction, may affect the dimensions and health of the peri-implant soft tissues and radiographic bone levels. SUBJECTS AND METHOD: Fifteen Astra Tech and 15 Brånemark single tooth implants that had been in function for a minimum of 2 years in 30 partially dentate subjects were examined for plaque accumulation, probing depth, and bleeding on probing and compared to contralateral healthy teeth. Standardized radiographs were taken to measure the most coronal bone to implant contact on the mesial and distal surfaces. In addition, samples of subgingival plaque were taken on paper points and examined by darkfield microscopy. RESULTS: Significantly higher mean probing depths (p < .001) and higher mean percentage of spirochetes (p = .003) were found at implants compared to teeth. In this sample, the Brånemark implants had significantly higher probing depths than the Astra Tech implants (median and interquartile range: Astra Tech 2.7 mm [2-3], Brånemark 3.3 mm [3-3.7] p = .026) and the most coronal bone to implant contact was closer to the implant-abutment junction in the Astra Tech implants (Astra Tech 0.6 mm [0.2-0.9], Brånemark 1.6 mm [1.4-2.0]. p < .001). CONCLUSION: Although there were statistically significant differences between the two implant systems, the clinical differences were small and probably reflect differences in the biologic width in relation to the location and design of the implant-abutment junction.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".