Does routine preoperative imaging of parotid tumours affect surgical management decision making?
Bibliographic record
Abstract
OBJECTIVE: To determine whether preoperative radiologic imaging of parotid tumours would alter the surgical approach to complete eradication of the parotid tumours. METHODS: A retrospective chart review of 173 patients with parotid tumours who underwent removal of parotid tumours at Mount Sinai Hospital in Toronto, Ontario, from 2000 to 2002. Each patient presented with a single, unilateral parotid tumour. Ninety-eight patients underwent preoperative radiologic imaging prior to definitive surgeries. The remaining 75 patients did not undergo preoperative imaging before surgery. RESULTS: Our study had shown that patients with superficial lobe parotid tumours did not require preoperative imaging as it did not affect the surgical management approach. On the contrary, for patients with parotid tumours clinically involving the deep lobe or parapharyngeal space, preoperative imaging often provides important additional information that may alter the surgical approach to eradication of parotid tumours. Moreover, patients with histopathologically benign tumours did not seem to benefit from preoperative imaging. On the other hand, patients with histopathologically malignant parotid tumours benefited from preoperative imaging as the additional information often altered the surgical management decision. CONCLUSION: Preoperative imaging should not be routinely ordered to investigate patients with parotid tumours. Patients with either deep parotid tumours or clinically suspicious tumours of malignancy would benefit from a preoperative radiologic investigation. The additional information from imaging may affect the surgical management decision.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".