Implant Placement at the Time of Mandibular Molar Extraction: Description of Technique and Preliminary Results of 341 Cases
Bibliographic record
Abstract
BACKGROUND: Theoretically, the ability to place implants in ideal positions at the time of mandibular molar extraction with concomitant regenerative therapy would simplify and shorten the course of therapy for patients. METHODS: A total of 341 implants were placed in 320 individuals at the time of mandibular molar hemisection and extraction. Concomitant regenerative therapy was performed around 332 of the placed implants. No regenerative therapy was performed around the remaining nine implants. Eleven additional sites, in which simultaneous implant placement was planned, were treated instead with regenerative therapy alone using graft material and a covering membrane. Implants were placed in these sites in subsequent surgical visits. RESULTS: One implant was mobile 3 weeks postinsertion. A second implant was lost after 30 months in function. All other implants were stable at the time of uncovery 3 to 7 months postinsertion. A total of 339 implants have been in function for up to 6 years, with a mean time in function of 30.8 months, yielding a cumulative survival rate of 99.1%. CONCLUSION: Implants may be placed in ideal restorative positions at the time of mandibular molar extraction with or without concomitant regenerative therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".