Implant Placement in Maxillary First Premolar Fresh Extraction Sockets: Description of Technique and Report of Preliminary Results
Bibliographic record
Abstract
BACKGROUND: The postextraction morphology of the maxillary first premolar extraction socket presents a number of challenges to clinicians seeking ideal implant position, including the morphology of the lateral walls of the extraction socket and the presence of the interradicular septum. A technique for simplification of implant placement at the time of maxillary first premolar extraction is described. METHODS: Sixty-three implants were placed in maxillary first premolar immediate extraction sockets in 57 patients (36 females and 21 males), utilizing a technique which includes removal of residual interradicular bone prior to preparing the osteotomy, use of the removed interradicular bone in the extraction socket defect surrounding the implant, and swaging of the buccal and palatal osseous plates against the implant. RESULTS: All implants demonstrated clinical stability upon uncovering. Forty-one of the implants placed have been restored and in function for a period of up to 2 years. CONCLUSIONS: The technique described affords a simplified and predictable manner for placement of implants into immediate maxillary first premolar extraction sockets. Further studies should be carried out to document long-term success and failure rates of implants placed utilizing this technique, and subsequently restored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".