Effect of Sleep Extension on the Subsequent Testosterone, Cortisol and Prolactin Responses to Total Sleep Deprivation and Recovery
Bibliographic record
Abstract
Total sleep deprivation (TSD) in humans is associated with altered hormonal levels, which may have clinical relevance. Less is known about the effect of an extended sleep period before TSD on these hormonal changes. Fourteen subjects participated in two experimental counterbalanced conditions (randomised cross-over design): extended sleep (21.00-07.00 h time in bed, EXT) and habitual sleep (22.30-07.00 h time in bed, HAB). For each condition, subjects performed two consecutive phases: six nights of either EXT or HAB. These nights were followed by 3 days in the sleep laboratory with blood sampling at 07.00 and 17.00 h at baseline (B-07.00 and B-17.00), after 24 and 34 h of continuous awakening (24 h-CA, 34 h-CA) and after one night of recovery sleep (R-07.00 and R-17.00) to assess testosterone, cortisol, prolactin and catecholamines concentrations. At 24 h of awakening, testosterone, cortisol and prolactin concentrations were significantly lower compared to B-07.00 and recovered basal levels after recovery sleep at R-07.00 (P < 0.001 for all). However, no change was observed at 34 h of awakening compared to B-17.00. No effect of sleep extension was observed on testosterone, cortisol and catecholamines concentrations at 24 and 34 h of awakening. However, prolactin concentration was significantly lower in EXT at B-07.00 and R-07.00 compared to HAB (P < 0.05, P < 0.001, respectively). In conclusion, 24 h of awakening inhibited gonadal and adrenal responses in healthy young subjects and this was not observed at 34 h of awakening. Six nights of sleep extension is not sufficient to limit decreased concentrations of testosterone and cortisol at 24 h of awakening but may have an impact on prolactin concentration.
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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".