Prevalence and management of neuropathic injury caused by dental implant insertion in mandible: a systematic review
Bibliographic record
Abstract
To synthesize scientific knowledge regarding the prevalence of neuropathies and nerve injuries caused by dental implant placement in mandible and the available management. Observational and interventional studies evaluating neuropathies occurrence in adults who underwent dental implant surgery were included. Any neuropathy diagnostic was accepted. The searches were conducted in six databases and grey literature. Methodological quality was screened using the Joanna Briggs Institute. The resulting synthesis was a narrative summary, and prevalence meta-analyses were performed in MetaXL 5.3. Among 98 full texts assessed, 38 studies were included. Neuropathies were diagnosed by questionnaires and/or clinical assessment. Eighteen studies presented high, sixteen moderate, and four low methodological quality. In implant surgeries without nerve lateralization, 12% and 5% of the patients may experience neuropathy during the first week and after three months, respectively. In implant surgeries with nerve lateralization, the prevalence was from 90% in the first week to 42% after three months. Proposed management included drugs, laser therapy and dental implant removal. In mandible, the prevalence of neuropathies in dental implant surgeries without lateralization is lower when compared with those with lateralization (eight times more in both follow-up times). The most frequent treatment was pharmacologic management.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".