Validity of a Nomogram to Predict Long Distance Running Performance
Bibliographic record
Abstract
The purpose was to test the validity of a nomogram to predict performance at distances ranging from the 10 km to the marathon. Official running rankings of the French Athletics Federation for the men's 10 km, 20 km, and marathon were scrutinized from 2002 to 2006. Performances of runners who competed in the 3 distances during the same year were noted (n = 330). Predicted performance by the nomogram was obtained for each distance from the performance at 2 other distances. Actual and predicted performances were compared by a Wilcoxon matched pairs test. The magnitude of the difference was assessed by the effect size (ES). Correlation and Bland-Altman plots were used to evaluate the association and the level of agreement between actual and predicted performances. The nomogram overestimated performance at the 10-km distance (13 seconds; p = 0.03) and underestimated performance at the 20-km distance (27 seconds; p < 0.01). The overestimation for the marathon was not significant (85 seconds; p = 0.06). Whatever the distance, ES were trivial (-0.04 < ES < 0.05). Correlations were 0.89 for the 10 km and the marathon and 0.97 for the 20 km. The limits of agreement represented 10.2, 6.1, and 13.2% of the mean of actual and predicted performances in 10 km, 20 km, and marathon, respectively. These results support the validity of the nomogram to predict performance on 10 km, 20 km, and marathon from the performance at 2 other distances. The accuracy of predictions is better when performance is interpolated. Given their validity and accuracy, interpolated predictions of the nomogram may be used to prescribe realistic training intensities during tempo runs, but also to determine the optimal strategy during the race.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".