Bibliographic record
Abstract
Physiotherapy has long followed a standard script. The patient is seen in-person at a clinic, a subjective history is taken and the physiotherapist completes a physical examination consisting of strength, range of motion, functional testing, etc to determine the cause of injury and prescribe an appropriate treatment plan. For running injuries, this assessment often includes an analysis—either on a treadmill or overground—of the patient’s running gait. When facilities and equipment are available, this may include three-dimensional (3D) motion capture and force plate analysis, which provides more detailed information about the biomechanics contributing to the presenting injury. Since most clinicians do not have access to this equipment, many use two-dimensional (2D) video analysis in the clinic. Recent circumstances have pushed many of us to explore remote options using online platforms, such as telehealth. This has forced us to be more creative with how we assess and treat patients and presents an opportunity to evolve our practice. With most runners having access to a high-quality video camera on their phone or tablet, 2D motion capture can be performed remotely. Recent advances and access to wearable technology—inertial measurement units (IMUs), in particular—now allow remote measurement of forces and spatiotemporal data. Remote biomechanical running gait analysis is now a reality (figure 1). Figure 1 Infographic: remote …
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.555 | 0.287 |
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".