High Age at the Time of Implant Installation is Correlated with Increased Loss of Osseointegrated Implants in the Temporal Bone
Bibliographic record
Abstract
BACKGROUND: The implant failure rate in temporal bone has been reported to be about 5 to 10% over a 10-year period. A number of our elderly patients have shown increased failure rates over a long time period, which is the reason for the present study. PURPOSE: The aim of the present study was to find out if age is correlated with implant failure and to measure blood flow in implant sites. MATERIALS AND METHODS: The long-time survival of 131 osseointegrated implants installed in the temporal bones of 81 patients was correlated with the age of the patient at the time of installation. The blood flow in 37 fixture installation sites in 22 patients was recorded by means of laser Doppler flowmetry. RESULTS: The mean implant failure rate in the study group was 9.8% after a mean follow-up time of 7.6 years. There was a significant increase of implant failure in patients above 60 years of age. There was further a trend that implants used for the bone-anchored hearing aid were lost to a higher proportion than implants used for bone-anchored episthesis. There was also a trend that female patients lost fewer implants than males. Blood flow in the temporal bone correlated well with the age of the patient in that the highest values were recorded from the youngest patients. CONCLUSIONS: Increasing age affects failures of osseointegrated implants in the temporal bone. Blood flow is higher in the child's temporal bone, a factor that can be of importance to understand why age influences implant survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".