Analysis of implant loss risk factors especially in maxillary molar location: A retrospective study of 6977 implants in Chinese individuals
Bibliographic record
Abstract
BACKGROUND: The knowledge of the potential risk factors associated with implant loss is crucial for dental clinicians, but the opinions about the risk factors are still diverse and controversial. PURPOSE: This retrospective study assessed the risk factors associated with implant loss, especially that in the maxillary molar location. MATERIALS AND METHODS: From January 2015 to March 2017, 4338 Chinese patients received 6977 implants at Nanjing Stomatological Hospital. Information on patient age, gender, bone grafting procedure, implant location, length and diameter, and the records of lost implants were obtained. The Kaplan-Meier method and log-rank test were used to conduct a survival function analysis. Chi-square test and multivariate Cox regression analysis were used to identify risk factors related to implant loss. RESULTS: The cumulative survival rate (CSR) after 0-32 months of observation period for all implants was 97.76%, and the CSR for maxillary molar implants was 97.00%. Maxillary molar implants showed a significantly lower CSR than the other implants (P < .05). Male sex, short implants (<10 mm) were considered as risk factors for implant loss. However, male sex and bone grafting procedure were regarded as risk factors for maxillary molar implant loss, which was slightly different from the result of all implants. CONCLUSIONS: Male sex, short implants (<10 mm) and maxillary molar location were considered as potential risk factors for implant loss, whereas male sex and bone grafting procedure were significantly associated with implant loss in maxillary molar location.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".