Evaluation of a tactile display around the waist for physiological monitoring under different clinical workload conditions
Bibliographic record
Abstract
In this study, we have assessed the usability of a tactile belt prototype for clinical monitoring of physiologic patient data in the operating room under low workload (LW) and high workload (HW) conditions. In previous investigations, we have evaluated tactile technology in clinical settings and demonstrated that anesthesiologists have enhanced situational awareness towards adverse clinical events when a tactile display prototype is used as a supplemental monitoring device. To further evaluate the effectiveness of our tactile belt prototype, we compared the effects of workload on the performance of anesthesiologists in terms of accuracy and response time in tactile alert identification. We also administered a post-study questionnaire to evaluate the usability of the tactile belt as well as users' opinions about the device. We found that the response time to tactile alert identification to be faster under LW than under HW, however the accuracy of identification was not statistically different. Participants rated the tactile belt prototype as comfortable to use and the tactile alert scheme as easy to learn. Our findings further support the feasibility and efficacy of vibrotactile devices for enhancing physiological monitoring of patients in clinical environments.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".