Benign Palatine Tonsil Volume Variation Following Bilateral Tonsillectomy in Adults
Bibliographic record
Abstract
Background There is a lack of data on normal size discrepancy in benign tonsils. The current school of thought for otolaryngologists is to remove tonsils that look clinically asymmetric on the basis of occult malignancy. However, many of these tonsils turn out to be benign after microscopic evaluation. The data in this article provide a reference range of size variation that can be seen in benign adult tonsils. Such new information can be incorporated into the surgeon’s preoperative discussion with patients with respect to informed consent and patient reassurance. Methods A chart review was conducted to identify pathology-proven benign bilateral tonsillectomies in the adult population. The review timeframe was from January 2012 to December 2017 (inclusive). All patients underwent surgery in an Eastern Health facility in Newfoundland and Labrador, Canada. In total, 403 cases were identified that fulfilled the inclusion criteria. Results Out of the 403 cases studied, the average tonsillar volume was 42.81 cm3. When differentiating between men and women, it can be seen that men have a higher average tonsil size (52.4 cm3) than women (37.85 cm3). The average difference in tonsil volume for all cases was 24.3%, with a standard deviation of 19.2%. Moreover, for men, the average difference in tonsil volume was 24.2%, with a standard deviation of 19.74%. Similarly, for women, the average difference in tonsil volume was 24.36%, with a standard deviation of 18.94%. Conclusions Findings from this study show that, on average, benign tonsils can vary in size by approximately 24% and that such a difference does not necessarily indicate malignancy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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, 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".