Designing an Electronic Personal Health Record for Professional Iranian Athletes
Bibliographic record
Abstract
BACKGROUND: By providing sports organizations with electronic records and instruments that can be accessed at any time or place, specialized care can be offered to athletes regardless of injury location, and this makes the follow-up from first aid through to full recovery more efficient. OBJECTIVES: The aim of this study was to develop an electronic personal health record for professional Iranian athletes. PATIENTS AND METHODS: First, a comparative study was carried out on the types of professional athletes'existing handheld and electronic health information management systems currently being used in Iran and leading countries in the field of sports medicine including; Australia, Canada and the United States. Then a checklist was developed containing a minimum dataset of professional athletes' personal health records and distributed to the people involved, who consisted of 50 specialists in sports medicine and health information management, using the Delphi method. Through the use of data obtained from this survey, a basic paper model of professional athletes' personal health record was constructed and then an electronic model was created accordingly. RESULTS: Access to information in the electronic record was through a web-based, portal system. The capabilities of this system included: access to information at any time and location, increased interaction between the medical team, comprehensive reporting and effective management of injuries, flexibility and interaction with financial, radiology and laboratory information systems. CONCLUSIONS: It is suggested that a framework should be created to promote athletes' medical knowledge and provide the education necessary to manage their information. This would lead to improved data quality and ultimately promote the health of community 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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".