Neurosensory outcomes following permanent lingual nerve injury in surgical removal of mandibular third molars: a systematic review and meta-analysis
Bibliographic record
Abstract
AIM: The preset systematic review and meta-analysis aimed to evaluate neurosensory outcomes following permanent lingual nerve injury in the surgical removal of mandibular third molars. METHOD: The international databases Cochrane, Embase, and MEDLINE (PubMed and Ovid) were searched for pertinent keywords until January 2025. Eight studies were included, and their risk of bias was evaluated using the Newcastle-Ottawa Scale. Using a random effect model and limited maximum-likelihood techniques of 95 percent confidence intervals (CI), prevalence rates were employed as an effect size. Stata (version 17) was used for meta-analysis. RESULT: The prevalence rate of somatosensory following lingual nerve injury after surgical removal of mandibular third molars was 5.58% (ES 5.58% 95% CI; 2.55%, 8.61%). The frequency of pain and gustatory was 36.78% and 3.52%, respectively. CONCLUSION: According to the present meta-analysis, the incidence rate of neurosensory outcomes is high after the surgical removal of mandibular third molars and following permanent lingual nerve injury.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".