Periodic health evaluation in Para athletes: a position statement based on expert consensus
Bibliographic record
Abstract
Para athletes present a broad range of sports-related injuries and illnesses, frequently encountering barriers when accessing healthcare services. The periodic health evaluation (PHE) is a valuable tool for continuously monitoring athletes' health, screening for health conditions, assisting in the surveillance of health problems by establishing baseline information and identifying barriers to athlete's performance. This position statement aims to guide sports healthcare providers in the PHE for Para athletes across key impairment categories: intellectual, musculoskeletal, neurological and vision. A panel of 15 international experts, including epidemiologists, physiotherapists, optometrists and physicians with expertise in Para athlete health, convened via videoconferences to discuss the position statement's purpose, methods and themes. They formed working groups to address clinical, cardiorespiratory, neuromusculoskeletal, nutritional status, mental and sleep health, concussion and female Para athlete health assessment considerations. The PHE's effectiveness lies in its comprehensive approach. Health history review can provide insights into factors impacting Para athlete health, inform physical assessments and help healthcare providers understand each athlete's needs. During the PHE, considerations should encompass the specific requirements of the sport modality and the impairment itself. These evaluations can help mitigate the common tendency of Para athletes to under-report health issues. They also enable early interventions tailored to the athlete's health history. Moreover, the PHE serves as an opportunity to educate Para athletes on preventive strategies that can be integrated into their training routines, enhancing their performance and overall health. This position statement can potentially enhance clinical translation into practice and improve the healthcare quality for Para athletes.
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 | 0.113 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".