Bibliographic record
Abstract
International travel is a frequent occurrence in the life of the elite athlete; such travel can pose challenges to the sport medicine practitioner. Travel is also the reality of many recreational level or sub-elite athletes as opportunities for international competition and training proliferate. An appreciation of the range of responsibilities associated with the preparation for and the strategies to facilitate such travel is essential for any physician charged with the care of athletes and teams. An appreciation of (1) the medical and public health challenges associated with competition in a particular setting; (2) the requirements for vaccination and immunization; (3) the strategies for the management of jet lag and climatic or environmental extremes; (4) the range of supplies and equipment necessary for travel to certain locales; (5) the need to ensure the availability of ample familiar and nutritious foods; (6) the potential need for specialty care in strange settings; (7) the management of common travel-associated illness; and (8) the challenges associated with the evacuation of an injured athlete are fundamental to the successful management of international travel involving athletes and teams. The adoption of a methodical approach to pre-trip planning can ensure an enhanced travel experience, illness-free training and competition, and facilitate optimal performance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".