Mineral Metabolism in Male Cyclists during High-Intensity Endurance Training
Bibliographic record
Abstract
This study examined the effects of intense endurance training on basal plasma and 24-hour urinary calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), and copper (Cu) levels in 9 male competitive cyclists. The supervised training program followed a baseline period and included a volume phase (6 weeks, averaging 87% of maximal heart rate [HR(max)]), an interval phase (18 days, 100% of HR(max)), and a 10-day unloading taper. The primary training outcome measure was 20-km time-trial cycling performance. Subjects ate unrestricted diets and maintained their weight. Compared to baseline, performance improved significantly (p < .05), while mineral metabolism was not significantly different after the volume phase. However, after the interval phase, renal Ca excretion increased (p < .05) and plasma Ca fell slightly below the clinical norm. As compared to the interval phase, urinary Ca decreased (p <.05), plasma Ca increased (p < .05), and performance further improved (p < .05) after the taper. Whereas Mg, Fe, Zn, and Cu metabolism remained unchanged throughout the study, greater renal Ca excretion was associated with very high intensity interval training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".