Performance Evaluation of a Salivary Amylase Biosensor for Stress Assessment in Military Field Research
Bibliographic record
Abstract
BACKGROUND: A convenient biosensor for real-time measurement of biomarkers for in-field psychophysiological stress research and military operations is desirable. We evaluated a hand-held device for measuring salivary amylase as a stress marker in medical technicians undergoing combat casualty care training using two different modalities in operating room and field settings. METHODS: Salivary amylase activity was measured by two biosensor methods: directly sampling saliva with a test strip placed under the tongue or pipetting a fixed volume of precollected saliva onto the test strip, followed by analyzing the sample on the strip using a biosensor. The two methods were compared for their accuracy and sensitivity to detect the stress response using an enzyme assay method as a standard. RESULTS: The measurements from the under-the-tongue method were not as consistent with those from the standard assay method as the values obtained from the pipetting method. The under-the-tongue method did not detect any significant increase in the amylase activity due to stress in the operating room (P > 0.1), in contrast to the significant increases observed using the pipetting method and assay method with a significance level less than 0.05 and 0.1, respectively. Furthermore, the under-the-tongue method showed no increased amylase activity in the field testing, while both the pipetting method and assay method showed increased amylase activity in the same group (P < 0.1). CONCLUSION: The accuracy and consistency of the biosensors need to be improved when used to directly measure salivary amylase activity under the tongue for stress assessment in military medical training.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".