Response to: ’Optimising the efficacy of gait retraining'
Bibliographic record
Abstract
A recent editorial was published in BJSM 1 following a randomised clinical trial (RCT) conducted by our research team, which compared three rehabilitation programmes in runners with patellofemoral pain (PFP).2 In Dr Davis’ editorial, it was stated that our running intervention was not optimal due to a heterogeneous sample, unstandardised gait modifications and unstructured retraining schedule. While several interesting arguments were presented, readers must keep in mind that the current level of evidence on gait retraining for injured runners is far from conclusive. Previous studies on gait retraining for runners with PFP have addressed specific running mechanics such as rearfoot striking or excessive hip adduction.3 4 However, not all runners with PFP show ‘altered’ mechanics, and PFP is not specific to rearfoot strikers (non-rearfoot strikers also develop PFP). In fact, training errors are thought to contribute to running injuries even in those with ‘optimal’ mechanics.5 Aiming to maximise the external validity of our RCT, we included runners regardless of distal or proximal kinematics, and prescribed individualised …
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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.046 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".