Treatment Outcome of Nasal and Paranasal Sinus Carcinoma
Bibliographic record
Abstract
PURPOSE: Most authors recommend aggressive management for sinonasal carcinoma treatment. In an attempt to determine the optimal treatment, we assessed the treatment results of our patients with nasal cavity and paranasal sinus carcinoma. MATERIALS AND METHODS: From January 1980 to December 2001, 40 patients with malignant tumours of the nasal cavity and the paranasal sinuses were treated. The median follow-up was 6 years. Thirty-two patients had tumours originating from the maxillary sinus. Thirteen patients had T1-T2 (32.5%) tumours and 27 patients had T3-T4 (67.5%) tumours. The treatment method was surgery plus radiotherapy in 24 patients (60%) and radiotherapy alone in 16 patients (40%). RESULTS: The 5-year overall survival rate was 61%, whereas it was 65% for T1-T2 disease and 56% for T3-T4 disease. The 5-year local control rate was 58%, whereas it was 75% and 50% (p = .219) for T1-T2 and T3-T4 disease, respectively. In multivariate analysis; localization (p = .016), adjuvant radiotherapy (p = .040), local control (p = .05), and gender (p = .013 for female) were statistically significant factors. CONCLUSION: The prognosis for patients with tumours of the sinonasal area is dependent on localization, tumour stage, and treatment modality. Because the most common site of treatment failure is the primary site, efforts to maximize local control should be undertaken.
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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 | 0.000 | 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) | 0.001 | 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".