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Record W4249977985 · doi:10.7324/japs.2018.8615

A Comparative Assessment to Evaluate Enhanced External Counter Pulsation Effect on Physical Profile and Quality of Life in Diabetic and Nondiabetic Coronary Heart Disease Patients

2018· article· en· W4249977985 on OpenAlexaboutno aff
Saurabh Dahiya, Vikram Singh, Girija Kumari, Bimal Chhajer, Ashok Jhingan

Bibliographic record

VenueJournal of Applied Pharmaceutical Science · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsCoronary heart diseaseMedicineCardiologyInternal medicineQuality (philosophy)Quality of life (healthcare)Physics

Abstract

fetched live from OpenAlex

This study was conducted to assess the effect of Enhanced External Counter Pulsation (EECP) on physical profile and Health-Related Quality of Life (HRQoL) in diabetic and nondiabetic Coronary Heart Disease (CHD) patients. This pretest-posttest designed prospective study was conducted among 163 diabetic and nondiabetic coronary heart disease patients in the SAAOL Heart Center, New Delhi. The physical profile of study subjects was assessed through Cooper's 12 minutes' walk test, Canadian Cardiovascular Society (CCS) angina scale and Medical Research Council (MRC) dyspnea scale. The HRQoL of subjects was assessed using SF-36 (short form) and Seattle Angina Questionnaire (SAQ) scale. A significant improvement was observed in blood pressure, heart rate, SpO 2 , VO 2 max, CCS angina and MRC score in both the groups from baseline to 12 months. Significant improvement was also observed in both the scales of HRQoL after EECP treatment at 12 months follow up in all the health domains of SF-36 & SAQ scale with special reference to angina severity and angina stability improvement. In conclusion, EECP is an effective non-invasive therapy to treat diabetic and nondiabetic CHD patients. This non-invasive procedure may improve the physical functional capacity, angina, dyspnea and overall HRQoL of diabetic and nondiabetic CHD patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.439
Teacher spread0.389 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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