Perioperative Management of the Sinus Patient: A Canadian Perspective
Bibliographic record
Abstract
OBJECTIVE: To survey the current practices and opinions of Canadian otolaryngologists with regard to the perioperative management of the sinus patient and to explore practice variations and examine the preferred methods of experts. DESIGN: A mailed survey was designed and sent to all members of the Canadian Society of Otolaryngology-Head and Neck Surgery who practice in Canada. The multiple-choice questionnaire addressed issues including diagnostic evaluation; routine preoperative, intraoperative, and postoperative methods; and practice demographics. RESULTS: A total of 242 questionnaires were returned, for an overall response rate of 72%. Preoperatively, the majority of surgeons obtained a computed tomographic scan (70%) and administered inhaled steroids (83%). Half of those surveyed performed endoscopic sinus surgery (ESS) using the image on the video monitor, and close to 70% routinely used postoperative nasal packing. There were significant variations in practice habits between the general respondents and a subgroup of self-defined "experts" in the field, defined as those who spent greater than 40% of their clinical time managing sinonasal disease. Analysis uncovered that the experts were statistically more likely to use preoperative systemic steroids (p = .008), use the video monitor (p = .045), and perform surgery under neuroleptic anaesthesia (p = .045). As a group, they were also less likely to routinely use postoperative place nasal packing (p = .004). CONCLUSIONS: Considerable variations in clinical practices were identified among Canadian surgeons. Continued efforts aimed at diminishing these variations through the establishment of evidence-based practice guidelines will assist in standardizing the care of these patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".