Logical fallacies in the running shoe debate: let the evidence guide prescription
Bibliographic record
Abstract
For the past 40 years, running shoes have been prescribed on the basis of matching shoe features to foot morphology to prevent running-related injuries (RRI). Yet, traditional shoe prescription has not prevented RRIs—consider five quality randomised controlled trials (RCT) and observational cohort studies.1–5 In contrast, a recent investigation6 found that motion control shoes protected against injury in experienced runners who had pronated feet. There are likely important methodological reasons for the discrepancies between these studies, such as differing definitions of RRI and various experience levels among runners. Nonetheless, there remains a lack of conclusive evidence to support traditional shoe prescription to prevent RRIs.7 Alternative shoe prescription paradigms have emerged. While minimalist shoes have historically received the most attention from researchers, clinicians and runners, the more recent paradigms of maximalism, zero-drop shoes and choosing a shoe based on comfort appear to be gaining in popularity (see figure 1 for examples). Figure 1 Examples of various shoe paradigms. Clockwise from top left: traditional (Brooks Epinephrine 18), minimalist (New Balance Minimus Trail 10), zero-drop (Altra Torin …
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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 | 0.036 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.027 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 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".