On Putting an End to the Backlash Against Electrophysical Agents
Bibliographic record
Abstract
Electrophysical agents (EPAs) are core therapeutic interventions in academic physical therapy curricula around the world. They are used concomitantly with several other therapeutic interventions such as exercise, manual therapy techniques, medications, and surgery for the management of a wide variety of soft tissue disorders. Over the past decade, the practice of EPAs has been the subject of intense scrutiny in the U.S. This has been colored by some physical therapists publicly engaging in bashing rhetoric that has yet to be officially and publicly addressed by the guiding organizations which, together, regulate the practice of physical therapy in this country. Published in world renowned public media are unsubstantiated mocking remarks against the practice of EPAs and unethical allegations against its stakeholders. This rhetoric suggests that EPA interventions are "magical" treatments and that those practitioners who include them in their plans of care may be committing fraud. Such bashing rhetoric is in striking contradiction to the APTA's Guide to Physical Therapist Practice 4.0, which lists EPAs as one of its categories of interventions, the CAPTE's program accreditation policy, and the FSBPT's national licensing exam. The purpose of this commentary is to expose the extent of this discourse and to call to action the APTA, CAPTE, and FSBPT organizations, as well as physical therapists, with the aim at putting an end to this rhetoric.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.046 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.039 | 0.049 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".