Evidence map›Paper›PMID 32676471›Full record

ArticlePreventive nutrition and food science2020

Glycemic Index and Sensory Evaluation of Whole Grain Based Multigrain Indian Breads (Rotis).

Raju Nagaraju, Prasanthi Prabhakaran Sobhana, Deepika Thappatla, Srinivas Epparapalli, Bhaskarachary Kandlakunta, Damayanti Korrapati

Abstract read
In one paragraph

Article in Preventive nutrition and food science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Raju NagarajuDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.
Prasanthi Prabhakaran SobhanaDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.
Deepika ThappatlaDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.
Srinivas EpparapalliDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.
Bhaskarachary KandlakuntaDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.
Damayanti KorrapatiDepartment of Dietetics, National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, Telangana 570001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole grain-based foods have been shown to reduce the risk of development of metabolic syndrome. In this study, we formulated whole grain-based multigrain flour and analyzed for available carbohydrate content, glycemic index (GI), and sensory evaluation. The multigrain flour composition 1 (C1) and composition 2 (C2) were formulated using defatted soya or bengal gram as a source of protein along with millets (40∼45%) and whole cereals. The proximate composition was calculated using Indian food composition tables. The microbial load and free fatty acid contents were analyzed in flour samples that were stored for different durations. The total dietary fiber, protein, and carbohydrate contents per 100 g of C1 and C2 flours were in the range of 11∼14, 13∼15, and 60 g, respectively. The available carbohydrate content in C1 and C2 were 55.4 and 62.3 g, and the

Indexed as

glycemic carbohydratein vivo glycemic indexmetabolic syndromemultigrain floursensory evaluation

Identifiers

PMID32676471
PMCPMC7333014

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.