A Novel Vibrotactile Display to Improve the Performance of Anesthesiologists in a Simulated Critical Incident
Bibliographic record
Abstract
BACKGROUND: Current methods of information transfer in the operating room between monitor and anesthesiologist rely on visual and auditory modalities. These modalities can easily become overloaded in a high cognitive workload situation, such as in a critical incident. The use of vibrotactile communication has been shown to improve information transfer in other high cognitive workload environments such as aviation. We designed a novel waist-mounted vibrotactile display to be worn by the anesthesiologist to test if a vibrotactile display could improve the clinical response time to begin treating a simulated case of anaphylaxis when compared with a group using traditional information displays. In addition, we evaluated differences in situational awareness (SA) between the two groups. METHODS: Twenty-four volunteer anesthesiologists were randomized to diagnose and treat a simulated case of anaphylaxis using the vibrotactile display and standard monitoring (vibrotactile display group) or standard monitoring alone (control group). The time taken to administer epinephrine was measured, and objective post hoc analysis of participant SA was performed. RESULTS: Participants in the vibrotactile group took 4.08 min (95% CI = 1.22) to deliver definitive treatment compared with 7.21 min (95% CI = 2.07) for the control group (P < 0.05). Despite the reduced time to treatment, no improvement in SA was measured. CONCLUSION: Our study provides evidence that vibrotactile communication can reduce response time to critical incidents.
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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".