The Current Limitations and Future Direction of Instrument Design for Totally Endoscopic Ear Surgery: a Needs Analysis Survey
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify limitations and challenges associated with existing instruments and techniques used in totally endoscopic ear surgery (TEES). BACKGROUND: Otologic instruments, traditionally developed for two-handed surgery with operating microscopes, are not necessarily optimized for the TEES environment. Better understanding of technical challenges and the limitations of current instrumentation may allow advances in instrument design for TEES surgery. METHODS: This cross-sectional study employed a mixed-methods nine-question survey that was distributed internationally to surgeons with an interest in TEES. Respondents were asked to classify their TEES experience and instrumentation used, rate their need for better instrumentation to address six TEES-related challenges using visual analog scales, and comment on how to modify or develop new instrumentation. RESULTS: With 51 respondents, we quantified a need for better instruments to address the following 6 potential TEES challenges ordered from greatest to least need: 1) reaching structures visualized by the endoscope, 2) dissection and removal of cholesteatoma, 3) cutting and/or removing bone, 4) bleeding control, 5) keeping the endoscope lens clean, 6) moving and positioning a graft into the intended place. The majority of surgeons perceive a need for improved instrumentation to address each challenge. Challenges 1) and 2) were associated with significantly greater need than the others (p < 0.05, Wilcoxon method for nonparametric pairwise comparisons). CONCLUSION: In addition to highlighting and quantifying some of the common TEES challenges, these findings provide valuable insight into the design requirements for developing improved surgical instrumentation and techniques.
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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 | 0.055 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".