A Novel, Minimally Invasive Method to Retrieve Failed Dental Implants in Elderly Patients
Bibliographic record
Abstract
This practice-based study presents the clinical outcomes of a minimally invasive method for retrieving failed dental implants from elderly patients. Traditional removal methods for failed dental implants include trephination and other invasive procedures. That can be a special concern for the elderly, since aging exacerbates oral surgery-related morbidity and anxiety. This retrospective cohort study gathers data from 150 patients seen in a private clinic. Their implants (n = 199) failed due to biological, mechanical, or iatrogenic causes, and were removed as part of their treatment plan. Collected data included: (1) implant location (maxilla/mandible, anterior/posterior region), (2) reasons for implant retrieval, (3) connection type, (4) removal torque, and (5) operatory procedure—flapless and using a counter-torque removal kit, whenever possible. Flapless/minimally invasive retrieval was successful for 193 implants (97%). The remaining six implants demanded trephination (open-flap). The most common reasons for implant retrieval (81%) involved biological aspects, whereas iatrogenic (12%) and biomechanical (7%) reasons were less common. The surgical technique used was not associated to connection types or removal torque. Authors conclude that a counter-torque ratchet system is a minimally invasive technique with a high success rate in retrieving implants from elderly patients. Present findings support its use as a first-line approach for implant retrieval in that population.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".