Bibliographic record
Abstract
Recent literature has embraced the use of electrosurgery and endoscopy in adenoidectomy, with several published articles on the subject. The combination of these methods and the routine use of endoscopy have not been reported. This approach provides a direct-targeted route to the nasopharynx, improved visualization, and magnification and offers a bloodless surgical field. It allows improved evaluation of the adenoids with their peritubal extensions, their lateral and central portions, and their extension to the posterior nasal choanae and even in the posterior nasal fossae and evaluation of the posterior of the middle and inferior turbinates. It permits objective documentation of the cause of nasal obstruction with possible use in outcome assessment. It is also an effective teaching method and a motivating approach for the nursing team. Our approach has proved cost and time efficient in our minimally invasive surgical (endoscopic) operating room set-up. This article reflects the experience in a series of 96 consecutive patients performed during a 9-month period and discusses the surgical technique and patients' outcomes. The endoscope and suction cautery were systematically used for all adenoid surgery. Outcomes were evaluated using a telephone survey with a global rating questionnaire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".