Treatment Of Individuals With End-stage Renal Disease: An Examination Of Practice Patterns In Washington State
Bibliographic record
Abstract
Physical therapy has been shown to be beneficial for patients with end-stage renal disease (ESRD), however information regarding specific rehabilitation for this population is limited. This descriptive study examined the practice patterns of licensed physical therapists who treat patients with ESRD in the state of Washington. A questionnaire was sent to 1374 licensed physical therapists in the state of Washington with a 60% return rate. Twentytwo percent of the respondents indicated that they treat patients with ESRD, and those patients accounted for less than 25% of their total caseload. The majority of respondents routinely monitor physiological measures such as heart rate, blood pressure, rating of perceived exertion, and oxygen saturation when working with this population. Typical interventions include range of motion, strengthening and aerobic type activities as well as instruction in breathing exercises and energy conservation techniques. The Functional Impact Measure (FIM) is the most frequently used functional outcome measure. The multi-systemic nature of renal disease, coupled with the complexity of medical treatment, make individuals with ESRD challenging to manage in physical therapy. Understanding the types of interventions and outcome measures used may enable therapists to work more effectively with this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".