A Blueprint for High Altitude Acclimatization Prior to High Altitude Competition for Professional Athletes
Bibliographic record
Abstract
Introduction: Among professional athletes, high altitude training is a popular technique due to its documented success on improving cardiovascular health and athletic performance. Nevertheless, there is little consensus on the guidelines for high altitude training and competition. This review sought to summarize existing literature for acclimatization recommendations for competing at high altitudes and suggests a blueprint that could be followed by athletes and trainers. Methods: This paper is part of the Altitude Nondifferentiated ECG Study (ANDES) project. A non-systematic search was conducted using Pubmed, EMBASE and MEDLINE databases. Results: Six studies were included, all of which recommended a gradual ascent before competition. The duration of acclimatization ranged from 4 days to 2 weeks depending on the magnitude of ascent. Athletes are encouraged to have pre-ascent assessments of ferritin, transferrin, hemoglobin mass, ECG, and weight with close monitoring of adverse altitude-induced complications. Conclusion: This study provides insight on key recommendations for athletes and trainers to consider when training and competing at high altitudes. These strategies can optimize athletic performance and mitigate deleterious altitude effects that can hinder functionality and training.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".