DEVELOPMENT OF A NOVEL STEERABLE BOUGIE
Bibliographic record
Abstract
BackgroundThis paper describes the development of a new airway device that will improve the speed and safety of endotracheal intubation in anaesthesia and critical care.Case of need, design specification and fabrication of the steerable bougie mechanism is discussed. AimsIdentify the need for a novel steerable bougie whilst considering technology readiness levels associated with medical device design.Analyse and produce suitable mechanisms utilising smart materials to increase device functionality aiding successful patient intubation procedures. MethodsThis work describes the total design activity that contributes to the successful development of medical devices, from case of need, to smart material actuation mechanisms.Research focuses on identifying a suitable control mechanism to allow a steerable tip to be integrated into a bougie with a control device attached to the laryngoscope. ResultsData collected from a user group survey supported the development of a novel bougie, with better shape retention, variable rigidity within the tip, and an integrated steerable function.Analysis of several mechanisms, artificial muscles, and smart materials identified a cost-effective steerable mechanism that can be incorporated into a bougie. ConclusionUsers have defined a need for an improved bougie.Controlling smart materials and mechanisms, within the predefined dimensions, identified strengths and weaknesses associated with steerable functions.The performance of the selected mechanism for incorporation requires a high level of control to accurately steer a device within the human airway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".