Accuracy of Linear Measurement Provided by Cone Beam Computed Tomography to Assess Bone Quantity in the Posterior Maxilla: A Human Cadaver Study
Bibliographic record
Abstract
PURPOSE: The aim of this study was to assess, for implant placement in the posterior maxilla, the accuracy of linear measurements provided by cone beam computed tomography (CBCT) using an image intensifier tube and television (TV) chain as an X-ray detector despite a loss of contrast resolution. The NewTom 9000 (Quantitative Radiology, Verona, Italy) was used to explore the posterior maxilla. MATERIALS AND METHODS: Fourteen measurements were taken in three dry maxillaries. On every anatomical site, three fiducial markers were placed on the bony crest to define a plane. Dry maxillaries were submitted to CBCT imaging examination. The maxillaries were then sawn according to the previously defined planes, and bone height and width were assessed using a caliper. The same measurements were taken on images. RESULTS: Clinical analysis demonstrated no difference between real measurements and image measurements. CONCLUSIONS: Although cadaver bone density may not correspond to the density of vital bone, this in vitro study indicates that CBCT images provided by technique using image intensifier tube and TV chain as an X-ray detector are reliable to define the bone volume of the posterior maxilla for the purpose of planning the implant axis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".