Bibliographic record
Abstract
To the Editor: I am writing concerning “A Calibration Study of Therapeutic Ultrasound Units” in the March 2002 issue of Physical Therapy. This article addresses a number of important areas of concern in the use of therapeutic ultrasound in physical therapy. I was pleased to see this matter addressed in a major physical therapy journal. In the past I have noticed that the terms “ultrasonic intensity” and “power” sometimes have been used interchangeably in the physical therapy community, and this appears to be the case in this article. In addition, some of the information in this article is not correct, and this may have led to some incorrect conclusions. First, contrary to what was stated in the article, the standards for ultrasound therapy devices do not require an ultrasonic output accuracy of ±20% of the intensity set in the United States and of ±30% of the intensity set in Canada and by the International Electrotechnical Commission (IEC). The current standards for ultrasonic therapy devices in the United States1 and in Canada2 and as set by the IEC3 all require an accuracy of ±20% on the indicated ultrasonic output power. The standards of the IEC and the ones used in Canada also require a ±20% accuracy for the effective radiating area (ERA). The US standard requires only that the error in the ERA be indicated.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".