Dry Socket Prevalence and Risk Factors in Third Molar Extractions: A Prospective Observational Study
Bibliographic record
Abstract
Background Third molar extraction is a routine oral surgical procedure that is often complicated by the development of a dry socket (alveolar osteitis). This prospective observational study aimed to investigate the prevalence of dry sockets and identify associated risk factors and causes, contributing to a comprehensive understanding of the postoperative outcomes of oral surgery. Methods This study employed a prospective observational design with a 12-month follow-up period. Participants aged 18-40 years scheduled for third molar extraction were included, whereas those with coagulopathies, pregnant or lactating women, patients with vitamin deficiencies, and individuals on medications affecting healing were excluded. Data collection involved comprehensive assessments at baseline, intraoperative details, and postoperative evaluations at 48 hours, one week, and two weeks. Statistical analyses included descriptive statistics, chi-square tests, t-tests, or Mann-Whitney U tests, and logistic regression for the risk factor analysis. Results A total of 238 participants with diverse demographic characteristics were enrolled in this study. The prevalence of dry sockets increased progressively from 20.6% at 48 hours to 41.2% at two weeks post-extraction. Smoking, poor oral hygiene, and surgical technique emerged as significant risk factors, with corresponding odds ratios of 6.41 (95% CI: 2.86-14.36, p < 0.001), 9.53 (95% CI: 2.12-42.84, p = 0.003), and 3.27 (95% CI: 2.08-5.15, p < 0.001), respectively. Pain intensity, measured using a Visual Analog Scale, gradually decreased from 48 hours to two weeks post-extraction. Conclusion This study provides valuable insights into the prevalence and risk factors associated with dry sockets following third molar extractions. Smoking, poor oral hygiene, and poor surgical techniques were identified as significant contributors, emphasizing the importance of preoperative counseling and targeted interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".