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Record W4391224262 · doi:10.17352/raoa.000015

Holistic Treatment of Inflammatory Disease for the Mobility Impaired

2023· article· en· W4391224262 on OpenAlexfundno aff
M Laymon, K Laymon

Bibliographic record

VenueRheumatica Acta Open Access · 2023
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
FundersDalhousie University
KeywordsDiseaseMedicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Background: Previous studies have demonstrated the positive impact of yoga and deep breathing exercises on overall physical wellness. The effects of deep breathing alone on metabolic rate, weight, and cholesterol management have not been studied well. Objective: This study assessed changes in C-Reactive Protein (CRP) following a 60-day intervention of a 12-minute deep breathing program. Methods: Sixty-six participants with a BMI >27 kg/m2 and 18 years - 70 years were enrolled in this study. Participants were assigned to either the control or intervention group in a single-blind manner. The intervention group followed the novel deep breathing program, while the control group did not modify their lifestyle or exercise. Anthropometric measurements and metabolic markers were evaluated and compared between the two groups after 60 days. Results: After 60 days, the control group exhibited a mean increase change in CRP of 3.9%. The intervention group showed a mean reduction of CRP by 25%. Conclusion: A guided daily 12-minute-deep breathing program can lead to reductions in inflammation marker CRP even without additional lifestyle modifications or medication modifications. Further investigations are warranted to explore the effects of novel deep breathing programs on metabolic markers and elucidate the underlying mechanisms of CRP reduction.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.189
GPT teacher head0.460
Teacher spread0.272 · 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
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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