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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".