Caffeine maintains vigilance and improves run times during night operations for Special Forces.
Bibliographic record
Abstract
PURPOSE: This study examined the effects of caffeine (CAF) on vigilance, marksmanship, and run performance during 27 h of sustained wakefulness in Special Forces personnel. METHODS: There were 31 soldiers (29.8 +/- 5.4 yr, 86.4 +/- 8.6 kg) who were divided into placebo (PLAC, n = 15) and CAF (n = 16) groups. A 6.3-km control run was completed on the morning of Day 1. In the evening of Day 2, soldiers performed a control observation and reconnaissance vigilance task (ORVT) in the field. This 90-min task was repeated twice more between 02:00 and 06:00 on Day 3 during an overnight period of sleep deprivation. Marksmanship was assessed before and after the ORVT. PLAC or 200 mg of CAF gum was administered at 01:45, 03:45, and approximately 06:30 on Day 3. A final 6.3-km run commenced within 30 min of receiving the final dose. RESULTS: ORVT was maintained in CAF at control levels of 77 +/- 13% during the overnight testing. However, values decreased significantly for PLAC from 77 +/- 15% to 54 +/- 29% and 51 +/- 31% during the first and second overnight testing periods, respectively. CAF had no effect on marksmanship but improved 6.3-km run times by 1.2 +/- 1.8 min. Run times slowed for PLAC by 0.9 +/- 0.8 min from approximately 35 min during the control run; the changes in performance were significant between groups. CONCLUSIONS: It was concluded that CAF maintained vigilance and improved running performance during an overnight field operation for Special Forces personnel.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".