Retrospective clinical analysis of risk factors associated with failed short implants
Bibliographic record
Abstract
Abstract Background With advanced technology, short implants are more commonly used and have proven to have a relatively reliable curable efficacy. A consensus has not been reached regarding potential risk factors related to the loss of short implants. Purpose This large‐sample retrospective study concentrated not only on patient characteristics and medical procedures but also on the features of implants in order to uncover the risk factors associated with short implants. Methods Between 2014 and 2017, a total of 7001 implants were inserted at Nanjing Stomatological Hospital, Medical School of Nanjing University. Among the all, 1236 short implants were included after being evaluated according to the inclusion and exclusion criteria. In organizing the detailed information, seven variables including bone grafting procedure, age, gender, diameter of the implant, implant position, surface treatment, and definitive restorations were taken into consideration. The χ2 test, Kaplan‐Meier test, logistic regression, and multifactorial Cox regression analysis were employed to explore the risk factors. Results The cumulative survival rate of short implants was 96.36%, slightly lower than that of the standard implants (98.16%, P < .001). Most of the short implants (84.44%) were lost at the early stage, mainly because of infection. Based on the results, male gender, implants treated by titanium anodizing and single‐crown restoration increased the loss rate of short implants. Comparison of the short implants inserted into the maxillary and mandibular posterior area alone showed that the maxillary molar area was a risk factor for prognosis. Conclusions Male gender, TA surface treatment, and the presence of a single crown were associated with an increasing rate of short implants loss. Examination of the implant location focused on the posterior area revealed the maxillary posterior area to be a risk factor.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".