Caffeine maintains vigilance and marksmanship in simulated urban operations with sleep deprivation.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the effects of caffeine (CAF) on physical, vigilance, and marksmanship tasks in soldiers during a sustained 55-h field exercise. METHODS: There were 30 soldiers (23.6 +/- 4.5 yr, 81.8 +/- 10.3 kg) who were divided into a placebo (PLAC) and a CAF group. After a period of restricted sleep of 3 h during the first night, a period of sustained wakefulness began that ended at 11:00 of the third day. PLAC or CAF doses of 100 mg, 200 mg, 100 mg, and 200 mg were administered at 21:45, 23:45, 01:45, and 03:45, respectively. At 22:00 of day 2, subjects began two cycles of marksmanship, urban operations vigilance, and psychomotor vigilance (PVT) testing which ended at 06:00 of day 3. RESULTS: CAF maintained marksmanship vigilance at 85% throughout the second night as compared with PLAC, who significantly declined to 61.4 +/- 28.2% overnight. Marksmanship accuracy also decreased significantly in PLAC from 95.1 +/- 8.3% to 83.3 +/- 19.2%, but no change was observed in CAF. Urban operations vigilance decreased for both groups over the night, but the decrease was less for CAF (81.2 +/- 14.4% to 63.4 +/- 24.1%) compared with PLAC (77.6 +/- 19.2% to 44.0 +/- 30.2%). Reaction time and the number of major and minor lapses with the PVT significantly increased in PLAC but were unaffected in CAF. CONCLUSIONS: It was concluded that CAF was an effective strategy to sustain vigilance and psychomotor performance during military operations involving sleep deprivation.
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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".