Lingual tonsil lymphatic tissue regrowth in patients undergoing transoral robotic surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate a possible regrowth of lingual tonsil lymphatic tissue in patients submitted to lingual tonsil resection with transoral robotic surgery (TORS). STUDY DESIGN: Retrospective Study. METHODS: Medical records of patients surgically treated by means of TORS to remove excessive lymphatic tissue of the lingual tonsil were retrospectively reviewed. Postoperative endoscopic data after long-term follow-up were analyzed to investigate possible lymphatic tissue regrowth. Preoperative and postoperative lingual tonsil lymphatic tissue were classified according to the standardized Friedman's grading scale ranging from 0 to 4. RESULTS: Sixty-eight patients (41 male and 27 female; mean age = 51.3 years) were considered suitable for the study analysis. Clinical regrowth was observed in six (8.8%) patients: four (5.9%) and two (2.9%) patients with grade 2 and 3 lymphatic hypertrophy, respectively. No correlation between the grade of regrowth, the time interval from surgery, and the volume of lymphatic tissue removed was found. CONCLUSIONS: The lymphatic tissue regrowth after TORS resection appears to be very low. LEVEL OF EVIDENCE: 4 Laryngoscope, 129:2652-2657, 2019.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".