Utilization rates of Active Release Techniques® in the workplace: A descriptive study
Bibliographic record
Abstract
BACKGROUND: Workplace safety is a necessary and frequent topic of discussion for researchers, employers, and workers. It is estimated that annual losses caused by work-related injuries cost the United States $140 to $145 billion. Specifically, work-related musculoskeletal disorders (WMSD) have risen from just over 10% to 50% of the total claims from 1952 to 1996, accounting for more than 345,00 days lost and more than $57 million in WSIB costs in 2015. Novel approaches to managing WMSDs, such as Active Release Techniques®, need to be explored to ensure the ongoing health of workers. OBJECTIVE: This descriptive study provides the details of the 697,002 onsite treatments across 448 sites in North America between 2014-2018 provided by Active Release Techniques® Corporate Solutions (ARTCS) practitioners. The objective was to calculate the total number of treatments, cost per closed case, percent improvement, and the number of cases referred to worker's compensation. METHODS: All charting was done on the ARTCS EMR portal. The lead author was given a spreadsheet of the results sanitized of all identifying data in order to perform the aforementioned calculations. RESULTS: From 2014 to 2018, ARTCS providers opened 199,077 new cases, with an average cost to the employer of $306.69 USD per case and an average percent improvement in pain (VNPS) of 87.2%. The most frequently treated areas were the shoulder (32,574 cases), hip (6,633 cases), and low back (27,873 cases), respectively. A total of 48,946 cases were work-related pain/discomfort, of which 1,110 (2.27%) went on to worker's compensation. CONCLUSION: ARTCS in the workplace has shown a positive trend in terms of reducing pain intensity. The cost was low as compared to worker's compensation claims, as was the rate of cases (2.27%) referred to worker's compensation. Additional studies, including prospective data collection and a control group, are warranted to substantiate this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".