Tonsillectomy. A comparative study of dissection/snare vs suction-cautery.
Bibliographic record
Abstract
In an optimal situation, a surgical procedure would be one that generates minimal post-operative pain, incurs little or no bleeding, and allows the patient to return to their normal daily activities in the shortest time period. A tonsillectomy is one of the most common operations performed in the world. Various surgical procedures for tonsillectomy are performed with a wide array of opinions to support the pros and cons of each technique. OBJECTIVES/GOALS: To determine if there is a significant difference between two methods of tonsillectomy. METHODS AND MATERIALS: A prospective single blinded randomized control study using (i) A dissection/snare technique, and (ii) A suction-cautery method. Measured outcomes such as blood loss, surgical time, post-op pain, post-op hydration, pyrexia, and the length of time to resume normal daily activities will be assessed. RESULTS: In total, 50 patients were studied, 23 in the dissection/snare technique, and 27 in the suction cautery technique. Inclusion criteria was, the patient must be at least 2 years of age and not older than 16 years of age. Data was collected intra-operatively, at 2 and 4 hour post-op intervals, as well as a 2 week follow-up questionnaire completed by the parents. CONCLUSIONS: The suction cautery group had statistically significant differences in blood loss, surgical time and pain in the immediate post-operative period.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".