The nature of malpractice claims related to nerve damage after dental implants insertion in Israel during 2005–2020: A descriptive study
Bibliographic record
Abstract
INTRODUCTION: Dental implant placement is a routine practice in dentistry, with a possible uncommon risk of neurosensory injury. The present study analyzed all dental implant claims involving sensory nerve disturbances between 2005 and 2020 in Israel. The study was conducted to understand implant risk management better and improve the patient's safety. MATERIALS AND METHODS: All legal claims registered by Medical Consultant International (MCI) involving nerve damage claims between 2005 and 2020 were included in the study. The data included demographic details such as age, sex, event date, claim delivery date, and treatment settings. Information on the nerve damage included the damaged nerve, side of injury, and the number of implants performed in the same surgery. RESULTS: There were 218 claims regarding nerve damage out of 1154 claims for dental implant therapy. The mean age for nerve damage claims was 54.1 ± 11 years. There were more female than male claims (p = 0.02), with 87% of cases concerning damage to the inferior alveolar nerve (p < 0.0001), out of those molar areas being more frequently involved in nerve damage (64.3%, p < 0.0001). The left side was 1.4 times more frequent than the right side (p = 0.043). The risk for nerve injury was 7.4 times higher when placing multiple implants compared to single dental implant (p < 0.0001). CONCLUSIONS: Clinicians should be aware that placement of multiple implants, left-side implant placement, and patient gender may increase risk for a malpractice claim for neurosensory disturbances.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".