Paralympic Medicine: The Road to Rio
Bibliographic record
Abstract
Over the past 10 years, our team has attended numerous Paralympic games and International Paralympic Committee (IPC)-sanctioned events where we have accumulated the largest data set to date from elite athletes with spinal cord injury (SCI). This empirical evidence has allowed us to address critical questions related to health and athletic performance in these incredibly medically complex individuals. Namely, does autonomic function influence performance? Can we account for this with the present sport classification? How can we prevent the doping practice of self-inducing life-threatening episodes of hypertension to improve performance (termed "boosting")? How does extremely high participation in routine upper-body wheelchair exercise impact cardiovascular and cerebrovascular disease risk? Is it possible to improve the sport classification to level the playing field between athletes with and without autonomic dysfunction? Herein, we will narratively address these questions, and provide our perspective on future directions and recommendations moving forward. Our extensive clinical experience and comprehensive dataset suggest preserved autonomic function is critical for elite performance. We will explore how an easy-to-execute test may be able to predict which individuals are most likely to develop autonomic dysfunctions that may negatively affect their health and performance. We also will evaluate the possibility that a level playing field may be even more difficult to establish than once thought, considering the importance of not only voluntary movement to performance, but also autonomic function. Finally, we also will discuss new changes in screening guidelines at Rio to assess the occurrence of boosting, which is a banned practice by the IPC.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".