Bone level changes proximal to oral implants supporting fixed partial prostheses
Bibliographic record
Abstract
The success of oral implant treatment relies on the presence and maintenance of bone adjacent to implants. The monitoring of radiographic bone level changes provides valuable insight into the longevity of oral implants. The purpose of this study was to measure radiographic bone level changes proximal (mesial and distal) to Brånemark System) implants (Nobel Biocare AB, Göteborg, Sweden) supporting fixed partial prostheses. Measurements were used to determine mean bone loss for the first year of loading by the prosthesis and the mean annual bone loss for subsequent years. These results were then compared and contrasted with various characteristics of the individuals, treatment, and treatment outcomes. Fifty-five subjects with 69 fixed partial prostheses supported by 160 implants were followed over a 1 to 12-year period. A mean bone loss of 0.33 mm (SD 0.59) was measured for the first year of loading and a mean annual bone loss of 0.00 mm (SD 0.11) after the first year. The radiographic bone loss calculated for implants at the first year of loading was positively correlated with the mean annual bone loss thereafter. Males, younger individuals and those implants supporting distal extension prostheses lost significantly more bone in the first year of loading. Larger numbers of implants followed for longer periods of time are needed to further explore the effects of various aspects of treatment on bone loss.
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 | 0.000 | 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 | 0.000 |
| 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".