Analysis of the Characteristics of Patients Visiting the Tokyo 2020 Olympics Polyclinic
Bibliographic record
Abstract
OBJECTIVE: To evaluate the characteristics of patients who visited the Polyclinic during the Tokyo 2020 Olympics and analyze geographical and economic correlations with the number of clinic visits. DESIGN: Cross-sectional study. SETTING: Polyclinic during the Tokyo 2020 Olympics. PARTICIPANTS: Patients who visited the Polyclinic. INTERVENTION: Data from the electronic medical record system of the Polyclinic were extracted. MAIN OUTCOME MEASURES: The number of visits for each athlete or team official was calculated by country. Relationship between number of visits per patient and total number of team members, total health expenditure per capita, density of medical doctors, life expectancy at birth, and education expenditure per gross domestic product (GDP) were investigated. Independent variables related to medal tables were also investigated. RESULTS: The average number of visits per athlete was 0.67, and it was higher in athletes from non-high-income countries compared with high-income countries for both male and female athletes. Number of visits per athlete was higher in countries with low life expectancy at birth (95% CI, -0.16 to -0.02, P = 0.012) and education expenditure per GDP (95% CI, -0.17 to -0.04, P = 0.003). CONCLUSIONS: During the Tokyo 2020 Olympics, the number of visits to the Polyclinic per athlete was higher in countries with low life expectancy at birth and education expenditure per GDP.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".