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Evaluation of Supplementation of Ashgourd Fermented Beverage to Geriatric Population

2012· article· en· W2141685444 on OpenAlexvenueno aff
C.S. Devaki

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2012
Typearticle
Languageen
FieldNursing
TopicFood Science and Nutritional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationFermentationFood scienceAnthropometrySubclinical infectionPhysiologyEnvironmental healthBiologyInternal medicine

Abstract

fetched live from OpenAlex

Existing data indicates a significant morbidity among the aged and most may remain subclinical. Vegetables being good source of vitamins and minerals, further their concentration has been improved by fermentation process. In recent years elderly institutions are being more common and a need for nutritional studies to provide better service. Thus the present study was conducted on geriatrics population to see the effect of ashgourd fermented beverage on anthropometric, nutritional and biochemical status of institutional elderly inmates. Ashgourd Fermented beverage (180ml) was provided to inmates of experimental group with the instructions to drink in fasting condition as a first drink in the morning for 60 days and control group received no supplementation. The parameters which were selected to evaluate the effect of ashgourd fermented beverage were anthropometric data, nutritional status, dietary pattern, clinical, biochemical parameters such as fasting and post prandial blood glucose levels, lipid profile, haemoglobin level and medical histories of the inmates. The selected parameters were assessed before and after the supplementation to see the effect of fermented beverage. It can be concluded from the present study that supplementation of fermented beverage showed significant improvement in BMI grades, heamoglobin and reduced blood glucose level, triglycerides and improved HDL cholesterol levels in experimental group when compared with the control group.

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)0.004
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.116
GPT teacher head0.440
Teacher spread0.324 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

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