A 5‐year longitudinal cohort study on crown to implant ratio effect on marginal bone level in single implants
Bibliographic record
Abstract
BACKGROUND: A 5-year longitudinal cohort study was carried out to evaluate the influence of anatomical crown to implant ratio (CIR) on peri-implant marginal bone level (MBL) in single implants. MATERIALS AND METHODS: The longest possible implants, according to the availability of pristine bone, were inserted, one per patient, among periodontally healthy teeth in consecutively recruited subjects. CIR and MBL changes were measured on standardized radiographs. The relationship between MBL and multiple predictors was investigated. A statistical analysis suitable for mixed type distributions was conducted: for the discrete component a logistic regression model was used and for the continuous component the impact of the variables on MBL was examined by using robust nonparametric comparison tests. RESULTS: Seventy-eight dental implants were inserted in 34 mandibles and 44 maxillae, with one stage procedure in 40 cases and two stage in 38 cases. Thirty-five implants were <10 mm, while 43 were ≥ 10 mm long; 28 implants had a CIR ≤1 and 50 had a CIR >1. No drop-outs or implant loss were observed. Bone loss occurred only in a few cases, measuring less than 0.5 mm and being significantly more pronounced for implant length ≥10 mm, for lower CIR values and for the two stage procedure. CONCLUSION: Higher CIR values were not related to increased peri-implant bone loss; a <10 mm long implant insertion may be safely considered for reduced bone heights.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".