Computer or not? Use of image guidance during endoscopic sinus surgery for chronic rhinosinusitis at St Paul's Hospital, Vancouver, and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The advantages and limitations of image guidance systems for endoscopic sinus surgery are unclear. We report our experience and present a meta-analysis of the evidence. METHODS: We performed a retrospective analysis of endoscopic sinus surgery procedures performed with versus without image guidance. A total of 355 cases was included. Primary outcomes included complication rates and time to revision surgery. A literature search was conducted to enable identification and analysis of studies of similar comparisons. RESULTS: Within 1.5 years of the index sinus surgical procedure, the risk of revision surgery was significantly higher for patients treated with non-assisted versus computer-assisted endoscopic sinus surgery (p = 0.001). Meta-analysis did not indicate a reduction in complications or revision surgery procedures with the use of image guidance systems, although the majority of included studies showed a non-significant reduction in revision surgery. CONCLUSION: Our study offers some evidence that computer-assisted endoscopic sinus surgery may delay residual disease and reduce the requirement for revision surgery. Although this finding was not borne out in the meta-analysis, the majority of identified studies demonstrated a trend towards fewer revision procedures after computer-assisted endoscopic sinus surgery. This type of surgery may offer other advantages that are not easily measurable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".