A Novel Instrument for Endoscopic Ear Surgery With a Steerable Flexible Tip: A Pediatric Anatomical Validation Study
Bibliographic record
Abstract
Hypothesis: This study compares the reaching ability of two classes of transcanal endoscopic ear surgery (TEES) instruments when operating on difficult to access anatomical targets; two novel instruments with steerable flexible tips (SFT-A and SFT-B) and suction capability are compared with standard commercially available tools. Background: TEES surgeons identified the need for a new surgical instrument that can enable accessibility of all areas visualized by the endoscope. This motivated the development of the two instrument prototypes. Methods: Six temporal bone models were 3D printed based on CT data from five cholesteatoma patients. Four anatomical targets were marked on each model. Using these targets, the reaching ability while using four standard TEES instruments were compared with the SFT-A and SFT-B prototypes by five surgeon participants. Results were analysed to compare success rates of contacting each target using each tool by fitting four Firth's logistic regression models. This calculated the statistically significant differences (p < 0.05) in tool success rate. Results: Using SFT-A to contact the sinus tympani (100%) was significantly more successful than the Panetti suction dissector for atticus (PAT) (77%) and to contact the sinodural angle (0%) was less successful than the PAT (10%) and SFT-B (93%). Using SFT-B to contact the lateral semicircular canal (90%) was significantly more successful than all current tools and to contact the sinodural angle (93%) was significantly more successful than all tools. Conclusion: Using SFT-B enables enhanced accessibility of anatomical structures during TEES which may lead to less extensive bone removal to facilitate minimally invasive TEES.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".