Plasma Volume Variations in Professional Soccer Players: Difference Between Pre- and Competitive Season
Bibliographic record
Abstract
Background: Variations in plasma volume and hematological parameters occur before and after training in soccer players. However, there are no reports on changes in hematological parameters resulting from a half-season of training in professional soccer players. Objectives: To investigate the effects of training load on plasma volume variations in elite soccer players. Methods: Twenty soccer players from the 1st French division soccer league (Ligue 1) were included in the study. The training load was evaluated using the rating of perceived exertion (RPE, 10- Borg scale) after each training session and each match. Anthropometrics characteristics, hematocrit (Ht), hemoglobin (Hb) and plasma volume variations (PVV) were assessed at 3 different times: T1: Baseline (before the first week of pre-season), T2: At the end of pre-season (after 8 weeks of training) and T3: At the end of the first competitive period (after 26 weeks of training and at halfway of the competitive season). Results: Values of Ht and lean body mass (LBM) increased during the first competitive period from T1 to T3 (Ht: ∆↑8%, P = 0.037, effect sizes (ES) = 1.0; LBM: ∆↑4%, P = 0.041, ES = 0.83) and from T2 to T3 (LBM: (∆↑2%; P = 0.05; ES = 0.77). Moreover, PVV decreased from T2 to T3 (P = 0.002, ES = 0.5) and from T1 to T3 (P < 0.05; ES = 1.26). There were no differences in Hb at T1, T2 or T3. Conclusions: Changes in body weights, PVV and Ht during preparatory and competitive phases were affected by the training load and competitive play.
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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 | 0.001 |
| 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) | 0.002 | 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".