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Record W3163442994 · doi:10.31189/2165-7629-10.1.1

The Final Frontier(s)?

2021· article· en· W3163442994 on OpenAlexaffabout
Jonathan K. Ehrman

Bibliographic record

VenueJournal of Clinical Exercise Physiology · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsMedicineReimbursementRehabilitationPopulationDiseaseAerobic exercisePhysical therapyCoronary artery diseaseIntensive care medicineInternal medicineHealth care

Abstract

fetched live from OpenAlex

Have we reached the pinnacle of insurance-approved chronic diseases for supervised exercise therapy (SET)? This is true only if the clinical exercise physiology (CEP) community is satisfied that no other populations would benefit from SET. I have been a clinical exercise physiologist for 35 years (although my job title in my early career was exercise specialist). In the mid-1980s in the US, cardiac rehabilitation was becoming an important part of the treatment plan for those with ischemic heart disease. At the time this was essentially the sole practice location and patient population for those with a CEP background. And while today cardiac rehabilitation programs remain the primary clinical space where clinical exercise physiologists practice, the breadth of the clientele has expanded. Participants with insurance coverage for cardiac rehabilitation and SET has expanded to include heart failure/transplant, valve procedures, and peripheral artery disease. Concurrent with the exponential growth in the understanding of the benefits of physical activity and fitness, much of these additions have come in the past 10 to 15 years, after years of stagnation. For instance, approval for SET for peripheral artery disease was approved in 2017. The following are several chronic diseases with the potential for future reimbursement approval.Exciting work is ongoing in those who are being treated for cancer or who are in the survivor phase. Exercise during treatment can alleviate symptoms and improve physical functioning (1). While we have understood for some time that exercise has a preventive effect on risk for development of several types of cancer (2), there is emerging data supporting links between physical activity and exercise affecting physiology, suggesting mechanisms to improve cancer risk (3) and treatment response (4).Kidney disease is a continuum ranging from chronic dysfunction to end-stage/dialysis to transplantation. Many patients currently participating in cardiac rehabilitation have renal disease. While the literature is mixed with respect to effects on improving kidney function (5), we do know that there is no harm to kidney function. There is also good evidence that exercise capacity improves in these patients (6).Additionally, improvements in blood pressure and diabetes control, as well as body composition may reduce the risk of further kidney function deterioration and cardiovascular complications that plague these individuals. In this population, as well as for those with cancer, low-level exercise may be performed during treatment (i.e., during dialysis or chemotherapy, respectively), affording increased clinical setting opportunities to provide SET.A recent pilot study provided preliminary data that exercise training performed in a cardiac rehabilitation setting improves physical functioning in stroke survivors (7). Importantly those suffering a stroke are at risk for cardiovascular complications, including another stroke and death. Supervised exercise following any necessary physical rehabilitation (i.e., physical therapy) might be important to improve exercise participation (which is very low), adherence, and benefits. Stroke rehabilitation integrated into cardiac rehabilitation is gaining traction in Canada (8,9), and in Australia those who have had a stroke are encouraged to seek an accredited exercise physiologist for assessment and exercise training.Importantly, for many individuals with these conditions, opportunities for safe and effective exercise in the community rarely exists. With continued focus on these populations by the CEP community, and particularly on the benefits of supervised exercise training, I believe these diagnoses have the potential for future reimbursement in the US. Importantly, improvements in mortality data is not necessarily required. In fact, this type of data is lacking for several cardiac rehabilitation indications including heart transplant, valve replacement, and peripheral artery disease. However, the evidence for each of these conditions is generally excellent for improvements in physical functioning and quality of life. While morbidity and mortality outcomes may be forthcoming, I believe the CEP community needs to focus on the other outcomes in the quest to gain reimbursement approval. In the meantime for those practicing in a cardiac rehabilitation setting, consider opening your maintenance (phase 3) program to individuals with isolated (or comorbid) cancer, renal disease, and stroke. And consider adding to the literature by collecting real-life data or designing your own small-scale study. These can help lead to definitive studies that may develop enough evidence to convince the decision-makers to approve coverage for SET in these and possibly other chronic disease/condition populations. Henry Ford Hospital, for instance, has opened a state-of-the art precision cancer treatment hospital and located an exercise training facility on the first floor. Additionally, we are developing a dialysis center and considering exercise opportunities in the design. These types of opportunities don't come along often, so be prepared as your institution might be open to these offerings. Seek these opportunities and be the champion promoter. The current climate is right to forge into these frontiers of CEP.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.099
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0130.018
Open science0.0020.007
Research integrity0.0130.018
Insufficient payload (model declined to judge)0.0990.034

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.

Opus teacher head0.064
GPT teacher head0.395
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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