The Kepler Intubation System
Bibliographic record
Abstract
Our goal in this study was to develop a robotic intubation system and to conduct a feasibility pilot study on the use of a robotic intubation system for endotracheal intubations. The Kepler Intubation System was developed, consisting of a remote control center (joystick and intubation cockpit) linked to a standard videolaryngoscope via a robotic arm. Ninety intubations were performed by the Kepler Intubation System on an airway trainer mannequin by a single operator. The first group of 30 intubations was performed with the operator in direct view of the mannequin (direct view group). The second group of 30 intubations was performed with the operator unable to see the mannequin (indirect view group). Thirty semiautomated intubations were also performed during which the robotic system replayed a trace of a previously recorded intubation maneuver (semiautomated group). First-attempt success rates and intubation times for each trial were recorded. Trends were analyzed using linear regression. Data are presented as mean (SD). All intubations were successful at first attempt. The mean intubation times were 46 (18) seconds, 51 (19) seconds, and 41 (1) seconds for the direct view, indirect view, and semiautomated group, respectively. Both the direct and indirect view groups had a negative slope, denoting that each successive trial required less time. The semiautomated group had a slope of 0 and a low SD of 1 second, illustrating the high reproducibility of automated intubations. We concluded that a robotic intubation system has been developed that can allow remote intubations within 40 to 60 seconds.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".