Determination of Bone Quality of 372 Implant Recipient Sites Using Hounsfield Unit from Computerized Tomography: A Clinical Study
Bibliographic record
Abstract
BACKGROUND: The type and architecture of bone are very important factors in the successful implant treatment, and it is manifested that higher implant failure is more likely in the poorer quality of bone. Conventional bone classifications have recently been questioned because they are subjective and retrospective. PURPOSE: This clinical study aimed to determine the variations of the bone density in dental implant recipient sites using computerized tomography (CT). MATERIALS AND METHODS: The study group comprised of randomly selected 140 patients with 372 implant sites. Recipient sites for implant placement were determined based on CT data using implant planning StentCad software (Media Lab Software, La Spezia, Italy). The mean bone density values in Hounsfield unit (HU) of the simulated implant areas were recorded using the StentCad software. RESULTS: The HU values ranged from 68 to 1,603 HU. It was found that mean bone density values were 927 +/- 237, 721 +/- 291, 708 +/- 277, and 505 +/- 274 HU in the anterior mandible, posterior mandible, anterior maxilla, and posterior maxilla, respectively. CONCLUSION: Preoperative CT examination may be a useful method for determining the bone density of recipient areas before implant placement, and this valuable information about bone quality helps clinicians to make better treatment planning regarding the implant positions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".