The effect of Winter’s red line, angle of impaction, and radio-morphometric indices on surgical difficulty of impacted mandibular third molar: a prospective observational study
Bibliographic record
Abstract
Introduction:The most common oral and maxillofacial surgical procedure is the surgical extraction of impacted third molar in the mandible.Predicting third molar difficulty depends on various clinical, anatomical, and radiographic variables.Objectives: The purpose of this study was to correlate the Winter's red line, angle of impaction, and bone density (gonial and antegonial indices) with surgical difficulty.Material and methods: This prospective observational study included 55 patients, who had undergone surgery for removing impacted lower third molars under local anesthetic.Independent (predictor) variables were pre-operative measurement of Winter's red line in millimeters, angle of impaction in degrees, and bone density evaluated with two radio-morphic indices, i.e., gonial and antegonial, both measured in millimeters.Dependent (outcome) variables included surgical difficulty determined by surgical technique and operation time.Results: Regarding the red line and angle of impaction, there was a strong positive correlation between them with duration of surgery.With respect to density indices, there was a weak correlation between gonial and antegonial indices with duration of surgery.All variables were associated with an increased difficulty determined by surgical technique.Conclusions: The Winter's red line and angle of impaction have a strong influence on determining the difficulty of impacted mandibular third molar's surgery, while the gonial and antegonial indices are not clinically relevant to determine the surgical difficulty.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".