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 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".