완전 무치악 환자에서 Locator attachment를 이용한 하악 임프란트 유지형 피개의치 수복 증례
Bibliographic record
Abstract
The primary objectives in treating fully edentulous patients are: recovery of function, such as mastication or pronunciation, esthetics, including facial profile, and the maintenance of health.\nIn this case, a fully edentulous patient was treated with an conventional complete denture for the maxilla and implant retained overdenture using Locator attachments in the mandible, following recommendations from the 2002 McGill Consensus. Clinical factors to consider before implant retained overdenture restorations are as follows: vertical space, economic factors, ease of cleaning, retention and lifespan of the attachments.\nThe interocclusal space in this case was about 8 ~ 9mm and the soft tissue was analyzed using the patients’ temporary dentures. Because of the insufficient interocclusal space, Locator attachments, which require the least amount of interocclusal space, were chosen. Locator attachments are also advantageous in patients who are shifting from wearing fixed type prosthesis to removable prosthesis, because of the easily adjustable retention strengths.\nFor esthetics and function, correct understanding of the patient’s chief complaint and the proper evaluation of existing restorations is crucial. In addition, when treatment planning, one must take into consideration the expectations of the patient and also the cost of treatment. For a better prognosis, periodic follow up must take place following treatment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".