Long‐term structural changes and outcomes of implants in particulate cellular bone and marrow reconstructed jawbone
Bibliographic record
Abstract
BACKGROUND: Association between long-term structural changes of grafted bone and outcomes of the implants is not clear. PURPOSE: Morphometrical measurements and implant success were analyzed in subjects of particulate cellular bone and marrow (PCBM) reconstruction of the jawbone. MATERIALS AND METHODS: Subjects were 30 implants in 13 patients from a series of 24 PCBM reconstruction cases. The cortical bone thickness and cancellous computed tomography (CT) radiodensity values were retrospectively analyzed from the CT data of 27 subjects. The cumulative success rate of the implants in the PCBM reconstructed cases (pure graft) was compared with that of 127 implants of 56 native bone cases and 42 implants of 28 bone augmentation (partial graft) cases. RESULTS: In areas of PCBM reconstruction, cancellous CT radiodensity values were significantly high immediately after the surgery, and subsequently the values became stable. Cortical thickness was significantly increased, but did not reach that of native bone. Implant success rates were statistically not different (P = 0.783) between the native bone (91.3%), the partially grafted bone (95.2%), and the PCBM reconstructed bone (93.3%). CONCLUSION: Implants in pure grafted bone are mainly supported by cancellous bone, because cortical thickness remained thin for a long period of time.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".