Perceptions and use of computer-assisted surgery (CAS) in the orbit
Bibliographic record
Abstract
Computer-assisted surgery (CAS) plays a prominent role in certain surgical disciplines. We investigated the current perceptions and use of this technology for orbital surgery. An online survey was emailed to members of the American Society of Ophthalmic Plastic and Reconstructive Surgery, Canadian Society of Oculoplastic Surgery, and British Oculoplastic Surgery Society. Respondents were asked to describe their practice type and seniority, their frequency of orbital surgery, experience, use, and accessibility of CAS, and their opinion on the technology. There were a total of 151 responses across the societies. 105 respondents (69.5%) had been in attending/consultant practice for over 10 years, with over half (54.7%) working in academic/teaching hospitals. The majority (66.7%) had superficial or no experience with CAS. In total, 84.8% of respondents rarely or never use CAS for orbital surgery (n = 128). Posterior orbital surgery (64.2%) and orbital decompression (49.0%) were the two most useful reasons to implement CAS. Longer operating time (58.3%) and cost (54.8%) were the two most selected weaknesses for CAS, whereas improved accuracy in attaining surgical end point(s) (80.8%) and patient safety (63.6%) were the principal advantages. Type of practice was significantly associated with CAS availability/accessibility (<i>p</i> < 0.05). Proportion of orbital surgery performed in practice was significantly associated with both CAS experience and use (<i>p</i> < 0.05). Our study confirms an expected variation in the perception and use of CAS for orbital surgery. Demonstrated patient benefit and integration of refined and cost-effective CAS systems into operating room environments may influence its future role.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".