A Retrospective Analysis of the Effectiveness of the Longevity Protocol for Assessing the Risk of Implant Failure
Bibliographic record
Abstract
BACKGROUND: A new, computerized diagnostic tool, called the Longevity Protocol, was recently developed to predict implant failure. The present retrospective analysis was undertaken to assess the prognostic validity of this protocol. MATERIALS AND METHODS: A selected group of patients who had been treated with implants over the past 10 years at six dental clinics and experienced implant failure were included in the analysis. Another group of patients with similar characteristics, not experiencing implant failure, was used as control. In April of 2015, data about each of the patients was entered into the Longevity Protocol database. For each patient, the risk assessment produced by the protocol was compared to whether the implants eventually failed. The implant failure predictions and actual implant failures were compared. RESULTS: The Longevity Protocol analyzed the possible failure of 595 implants placed in 221 patients (323 implants placed in 138 patients classified as low risk, 180 implants placed in 55 patients classified as moderate risk, and 92 implants placed in 28 patients classified as high risk). The actual percentage of implant failure in the three groups was 10%, 15%, and 22%, respectively. The differences between the groups were statistically significant. The sensitivity and specificity of the Longevity Protocol was 84.9% and 11.90% in the high/moderate risk group and 47.17% and 32.74% in the low risk group, respectively. CONCLUSIONS: Statistically significant results were obtained. The Longevity Protocol reliably identified patients who risked implant failure. The protocol appears to be an important tool for prognosis assessment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".