A NEW TREATMENT MODALITY TO REDUCE ACUTE TONSILLITIS HEALING TIME
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Acute tonsillitis is one of the most common reasons for application to otorhinolaryngology clinics. In the treatment of acute tonsillitis, supportive therapies are mostly used. As antibiotic therapy, penicillin or erythromycin can be used. The aim of this study is to decrease the clinical recovery time of acute tonsillitis by providing parenteral treatment and daily cleaning of tonsillar lesions. MATERIAL AND METHODS: Patients with an age range of 15-60 years were included in the study. The patients were divided into two groups. The first group used an i.v. combination of ampicillin + sulbactam and the tonsillar membranes of patients were cleaned daily. The second group used only the i.v. combination of ampicillin + sulbactam. RESULTS: Patients who received antibiotherapy and debridement had a clinical improvement of 90% on the 2nd treatment day and 95% on the 5th treatment day. The patients receiving only antibiotics had a clinical improvement of 65% on the 5th treatment day and 75% on the 7th treatment day. The recovery time of both groups was significantly different (p < 0.05). CONCLUSION: The solution and technique used in this clinical study showed that patients with acute tonsillitis could recover in a very short time without any complications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".