Clinical outcomes of ultrashort sloping shoulder implant design: A survival analysis
Bibliographic record
Abstract
BACKGROUND: Short implants are preferred in cases of limited bone height. Length and design of implant credits to stability and implant success, shorter implants tend to survive for longer time duration. PURPOSE: Retrospective cohort study was conducted with an aim to assess the clinical outcome and cumulative survival rate of sloping shoulder implants over a period of 8 years. MATERIALS AND METHODS: Data was collected from all patients attending private clinics in Dubai, UAE. Subjects received ultrashort sloping shoulder (Bicon) implant. Implants of 6 mm and less than 6 mm length were included in the study. Subject's information like age, gender, systemic condition, habits, and radiographs were collected. Implant variables that is number of implants placed, location of placement, loading type, bone type, bone condition, and graft type were collected. Data was analyzed using multivariate cox regression model to evaluate the correlation between implant variables and to identify the implant variables associated with failure. Kaplan-Meier method was adopted to assess the survival pattern of implants. RESULTS: Cumulative survival rate was 97% with average follow up of 28 months. Statistically significant differences were seen with implant length, arch type, bone type, and bone condition with P value <.001. CONCLUSION: Short implants with sloping shoulder design and plateau-type roots have superior survival rates when compared with regular implants. The bone condition was also witnessed to be statistically significantly superior.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".