Stable interindividual differences in modafinil’s effect on vigilance during sleep deprivation
Bibliographic record
Abstract
Rationale: In specific operational contexts (i.e., military aviation), the off-label use of modafinil is officially regulated. However, safety concerns are still raised. Objectives: To study the stability and robustness of interindividual differences in modafinil sensitivity, both in terms of risks and benefits in military student pilots. Methods: Eleven healthy military student pilots (21 ± 2 yr; 1 woman) were tested in a within-subject randomized counterbalanced crossover design to compare modafinil (2 × 200 mg; EXP) vs. placebo (CON) effects during extended wakefulness (24 h). Throughout both trials, participant's vital signs, mood, vigilance [i.e., Psychomotor Vigilance Task (PVT)] and self-monitoring ability were measured. Additionally, four participants were genotyped [i.e., COMT (rs4680) and PER3 (rs228697)]. We used Pearson correlation coefficients to evaluate the relationship between PVT performance and the performance self-monitoring scores. To evaluate the stability of interindividual differences in the effectiveness of modafinil to improve PVT performance and sleepiness, an intraclass correlation coefficient (ICC) was calculated for the delta score (CON-EXP) of both outcome measures. Results: Modafinil significantly improved PVT performance (p ≤ 0.034) and sleepiness (p ≤ 0.029) at 2a.m. and 4a.m. during the sleep deprivation night. The stability of the non-adjusted reaction time-delta score was very high (ICC = 0.90). Non-adjusted reaction time only correlated with the performance self-monitoring scores in CON (r ≥ -0.35; p < 0.001). Conclusion: Stable interindividual differences in the effectiveness of modafinil to counteract the sleep deprivation-associated decrease in vigilance exist. Further research should focus on quantifying the extent to which modafinil-induced overconfidence and subjective rebound sleepiness actually constitute potential problems in operational environments (e.g., perhaps using war game simulations).
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".