Identifying high cognitive load activities during simulated pediatric cardiac arrest using functional near-infrared spectroscopy
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Bibliographic record
Abstract
Aim: To identify specific activities associated with high cognitive load during simulated pediatric out-of-hospital cardiac arrest (POHCA) resuscitation using physiological monitoring with functional near-infrared spectroscopy (fNIRS). Methods: We recruited teams of emergency medical services (EMS) responders from fire departments located throughout the Portland, OR metropolitan area to participate in POHCA simulations. Teams consisted of both paramedics and emergency medical technicians (EMTs), with one paramedic serving as the person in charge (PIC). The PIC was outfitted with the OctaMon to collect fNIRS signals from the prefrontal cortex. Signals reported changes in oxygenated and deoxygenated hemoglobin concentrations, which were used to determine moments of increased cognitive activity. Increased cognitive activity was determined by significant increases in oxygenated hemoglobin and decreases in deoxygenated hemoglobin. Significant changes in fNIRS signals were associated with specific concurrent clinical tasks recorded by two independent researchers using video review. Results: We recorded cognitive activity of EMS providers in 18 POHCA simulations. We found that a proportion of PIC's experienced relatively high cognitive load during medication administration, defibrillation, and rhythm checks compared to other events. Conclusion: EMS providers commonly experienced increased cognitive activity during key resuscitation tasks that were related to safely coordinating team members around calculating and administering medications, defibrillation, and rhythm and pulse checks. Understanding more about activities that require high cognitive demand can inform future interventions that reduce cognitive load.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it