Original study. Effectiveness of endoscopic posterior nasal neurectomy for the treatment of intractable rhinitis
Bibliographic record
Abstract
Abstract BACKGROUND. Chronic rhinitis is a clinical condition affecting more than 20% of the world population. The standard treatment strategy is medical. Surgical management can be considered in patients with intractable rhinitis. Various surgical techniques have been documented with varying success rates, but none of them is considered as a gold standard. Hence, we are studying the effectiveness of posterior nasal neurectomy (PNN) in patients who have intractable rhinitis, refractory to maximum medical therapy. MATERIAL AND METHODS. A prospective study was conducted in the ENT Department, Padmavathy Medical Foundation, Kollam, Kerala, India, from January 2015 to February 2016. Adult patients, in the age group of 20 to 60 years, diagnosed with chronic rhinitis, presenting two or more symptoms of rhinitis, refractory to maximum medical therapy for a period of at least 3 or more years and whose quality of life was significantly affected were enrolled and PNN was done for them. RESULTS. We observed a statistically significant improvement in subjective symptoms and patient quality of life at the end of 6 months post-operatively. CONCLUSION. PNN is a safe and less invasive procedure, which can provide a significant relief in symptoms of intractable rhinitis. Fewer complications and better results make it superior over vidian neurectomy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".