A Study on Variances in Multivariate Analyses of Oral Implant Outcome
Bibliographic record
Abstract
BACKGROUND: Elaborate studies have shown that interdependency exists between implants being placed in the same patient/jaw. Therefore, interdependency ought to be an important aspect to address, whenever performing statistical analyses of oral implant outcomes. A Jackknife method could be an option when conducting statistical evaluations of oral implant failure prognoses. PURPOSE: The aim of this study was to evaluate whether a statistical difference can be detected by using the Jackknife method in conjunction with life table analyses and/or a log rank test of four different combinations of jaw density and quantity. MATERIALS AND METHODS: Four multicenter studies were pooled and adjusted in order to create a research database consisting of 486 patients and 1,737 implants in preparation for the Jackknife resampling method. Combinations of jaw shapes and bone qualities were constructed to select at-risk patients. STATISTICAL METHODS: Life tables with confidence intervals were calculated and a log rank test was used to determine whether a statistical difference between the combinations could be established. RESULTS: Both statistical analyses, after the Jackknife resampling method, showed that patients with poor bone quality and resorbed jaws (combination IV) had a statistically higher risk of implant failure. CONCLUSION: By rearranging data using the Jackknife method, standardized statistical tests seem to work well even when the study population tested was affected by interdependency.
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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 | 0.128 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".