Clinical, Aesthetic, and Patient‐Related Outcome of Immediately Loaded Single Implants in the Anterior Maxilla: A Prospective Study in Extraction Sockets, Healed Ridges, and Grafted Sites
Bibliographic record
Abstract
PURPOSE: The objective of this prospective clinical study was to document the overall treatment outcome of immediately loaded single Astra Tech Osseospeed™ (Astra Tech AB, Mölndal, Sweden) implants placed in extraction sockets, healed ridges, and grafted sites. MATERIALS AND METHODS: Forty-eight patients in need of a single implant in the anterior maxilla (15-25) were recruited. Patients were allocated to a conventional implant treatment (CIT) or immediate implant treatment (IIT) group on the basis of specific criteria. If the buccal bone plate was damaged or missing upon tooth removal, patients were allocated to a grafted implant treatment (GIT) group. Irrespective of the treatment concept, implants were immediately provisionalized. Hard and soft tissue alterations, aesthetic parameters (pink and white esthetic scores, [PES and WES]) and patient's opinion (Oral Health Impact Profile [OHIP-14] questionnaires) were registered at different time points. RESULTS: After 1 year of function, the overall implant survival rate was 98% with one failure following IIT. The mean bone level to the implant-abutment interface was 0.65 (SD 0.79), 0.85 (SD 0.64), and 0.56 mm (SD 0.44) for CIT, IIT, and GIT. Complete papilla loss was rare following either strategy. Mean midfacial recession amounted to 1.00 (SD 1.15), 0.12 (SD 0.78), and 0.49 mm (SD 0.82) for CIT, IIT, and GIT, respectively. The aesthetic outcome showed a mean PES of 10.30 (SD 1.89) and mean WES of 7.11 (SD 2.14), all patients considered. Patient's satisfaction showed a significant improvement after 1 year of function on all seven domains (p < .001). CONCLUSIONS: This prospective study showed that single implants clinically and aesthetically perform well under immediate non-occlusal loading conditions in the premaxilla. In this context, it is of pivotal importance to stress that patients were carefully selected for IIT and GIT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".