Selective Serotonin Reuptake Inhibitors and the Risk of Osseointegrated Implant Failure
Bibliographic record
Abstract
Selective serotonin reuptake inhibitors (SSRIs), the most widely used drugs for the treatment of depression, have been reported to reduce bone formation and increase the risk of bone fracture. Since osseointegration is influenced by bone metabolism, this study aimed to investigate the association between SSRIs and the risk of failures in osseointegrated implants. This retrospective cohort study was conducted on patients treated with dental implants from January 2007 to January 2013. A total of 916 dental implants in 490 patients (94 implants on 51 patients using SSRIs) were used to estimate the risk of failure associated with the use of SSRIs. Data analysis involved Cox proportional hazards, generalized estimating equation models, multilevel mixed effects parametric survival analysis, and Kaplan-Meier analysis. After 3 to 67 mo of follow-up, 38 dental implants failed and 784 succeeded in the nonusers group, while 10 failed and 84 succeeded in the SSRI-users group. The main limitation of this retrospective study was that drug compliance dose and treatment period could not be acquired from the files of the patients. The primary outcome was that compared with nonusers of SSRIs, SSRI usage was associated with an increased risk of dental implants failure (hazard ratio, 6.28; 95% confidence interval, 1.25-31.61; p = .03). The failure rates were 4.6% for SSRI nonusers and 10.6% for SSRI users. The secondary outcomes were that small implant diameters (≤4 mm; p = .02) and smoking habits (p = .01) also seemed to be associated with higher risk of implant failure. Our findings indicate that treatment with SSRIs is associated with an increased failure risk of osseointegrated implants, which might suggest a careful surgical treatment planning for SSRI users.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".