Altitude training causes haematological fluctuations with relevance for the Athlete Biological Passport
Bibliographic record
Abstract
The impact of altitude training on haematological parameters and the Athlete Biological Passport (ABP) was evaluated in international-level elite athletes. One group of swimmers lived high and trained high (LHTH, n = 10) for three to four weeks at 2130 m or higher whereas a control group (n = 10) completed a three-week training camp at sea-level. Haematological parameters were determined weekly three times before and four times after the training camps. ABP thresholds for haemoglobin concentration ([Hb]), reticulocyte percentage (RET%), OFF score and the abnormal blood profile score (ABPS) were calculated using the Bayesian model. After altitude training, six swimmers exceeded the 99% ABP thresholds: two swimmers exceeded the OFF score thresholds at day +7; one swimmer exceeded the OFF score threshold at day +28; one swimmer exceeded the threshold for RET% at day +14; and one swimmer surpassed the ABPS threshold at day +14. In the control group, no values exceeded the individual ABP reference range. In conclusion, LHTH induces haematological changes in Olympic-level elite athletes which can exceed the individually generated references in the ABP. Training at altitude should be considered a confounding factor for ABP interpretation for up to four weeks after altitude exposure but does not consistently cause abnormal values in the ABP.
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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) | 0.001 | 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".