Haematological and iron metabolism parameters in professional cyclists during the Giro d’Italia 3-weeks stage race
Bibliographic record
Abstract
BACKGROUND: Haematological assessment is crucial for evaluating athletes healthy status. Professional athletes experience physiological modifications during competitions and over a season: the risk of sports anaemia is high. Few descriptions of haematological parameters behaviour during a 3-weeks cycling stage race have been published. METHODS: We studied nine professional cyclists engaged in the 2011 Giro d'Italia stage race. Pre-analytical and analytical phases tightly followed academic and anti-doping authorities' recommendations. Haematological and iron metabolism parameters were measured days -1 (pre-race), 12 and 22 during the race. RESULTS: Haemoglobin, red blood cells and haematocrit decreased during the race with a stabilisation in the second half, but final values were lower than baseline. Reticulocytes did not modify, whilst the immature reticulocyte fraction increased. No differences were found in red blood cells volume and corpuscular haemoglobin content, neither in iron metabolism markers. The acute phase proteins, haptoglobin and C-reactive protein, both increased over the race, while haemoglobin and haptoglobin were inversely related. CONCLUSIONS: These data are important for improving the knowledge of physiological modifications in haematological and iron metabolism parameters of professional athletes during highly demanding competitions. This is the first report, in the ambit of a stage race, in which the pre-analytical phase standardisation has been applied.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".