Endoscopic management of inverted papillomas: long-term results, the St. Pauls Sinus Centre experience
Bibliographic record
Abstract
OBJECTIVES: To demonstrate that computer-assisted endoscopic management of inverted papillomas yields excellent long-term results in terms of preventing recurrence and minimizing significant morbidity and mortality. METHODS: A retrospective chart review of patients who are being followed up for tumour recurrence or have undergone tumour removal between 2000 and 2008. All cases were undertaken using the GE Instatrak 3500+ navigation system. RESULTS: Inverted papillomas are the most common tumour managed endoscopically (57% of all sinonasal tumours) with 76 patients seen over the last 8 years. Approximately 50% of these cases had undergone previous surgery in another centre where the tumour was either not recognized or the resection was incomplete. Twentynine percent of these patients had a recurrence but only three required a revision procedure using an open approach; otherwise recurrences were successfully managed endoscopically. Endoscopic recurrence during the first half was 32% (versus 14% for open procedures), dropping to a recurrence rate of 11% in the latter period. CONCLUSIONS: Endoscopic management of inverted papillomas allows good control of the disease and avoids unnecessary morbidity associated with open procedures. Although there is a higher initial recurrence rate, these recurrences can be successfully managed endoscopically, and computer navigation can be a useful adjunct in achieving this.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".