Evaluation of Respiratory Muscle Strength in Patients with Heart Failure
Bibliographic record
Abstract
Context: The estimated prevalence of heart failure (HF) is around 1% of the total population in India which is close by to 8–10 million individuals. Due to metabolic and structural skeletal muscle dysfunctions, peripheral muscles are impaired in the early stages of chronic HF (CHF). In experiments on rats with CHF, biopsies of respiratory muscles revealed histological abnormalities, such as atrophy of type 1 diaphragm fibers, resulting in decreased respiratory muscle strength and endurance. Aim: To determine the strength of respiratory muscle and pulmonary function in individuals having Class II and Class III HF. Settings and Design: Descriptive, observational, case–control study design. Subjects and Methods: Subjects included 37 HF patients having Class II and Class III in one group and controls in another group. The respiratory muscle strength (maximal inspiratory pressure [MIP] and maximal expiratory pressure [MEP]) was evaluated using respiratory pressure meter (RPM) and pulmonary function (forced expiratory volume at the end of 1 s [FEV 1 ] and forced-vital capacity [FVC]) assessment using spirometry-Schiller (micro RPM). Statistical Analysis Used: Median and IQR were used to describe the study variables MIP, MEP, FVC, and FEV1, and the Mann-Whitney U test to compare the study variables between the two groups. Results: The outcomes were interpreted as the median values. The MIP and MEP were 59 and 70 cmH 2 O, respectively, in HF as compared to 97 and 96 cmH 2 O in the control group, significant at P < 0.001. The FEV 1 and FVC in HF were 114 and 88% sequentially as compared to 130 and 99% in the control group, significant at P < 0.05. Conclusions: The respiratory muscle strength and pulmonary functions are impaired in individuals having Class II and Class III HF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".