Anatomic Landmarks for Locating Parotid Lesions in Relation to the Facial Nerve: Cross-sectional Radiologic Study
Bibliographic record
Abstract
OBJECTIVES: To determine the accuracy of using surrogate anatomic structures radiologically to predict the relation of parotid lesions to the intraparotid facial nerve. SETTING: Tertiary centre. DESIGN: Retrospective. PATIENTS AND METHODS: All patients with parotid masses over a 5-year period who undertook parotidectomy were considered. A radiologist and an otolaryngologist reviewed the images. Their decision regarding the location of the lesions using four surrogate structures was compared with intraoperative documentation. OUTCOME MEASURE: We determined the sensitivity and the specificity of using the external carotid artery, retromandibular vein, posterior belly of the digastric muscle, and tragal pointer. RESULTS: Thirty films were examined (24 magnetic resonance images [MRIs] and 6 computed tomographic [CT] scans). The sensitivity and the specificity of the retromandibular vein were 0.85 and 0.57, respectively, whereas for the external carotid artery, they were 0.94 and 0.3, respectively. It was too impractical to relate the other two structures to the lesions. CONCLUSIONS: The retromandibular vein is the most accurate surrogate structure to use on MRI or CT for predicting the location of a parotid lesion to the facial nerve. However, the substantial proportion of deep lesions misjudged limits the benefit of performing the imaging.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".