Evidence map›Paper›PMID 36385111›Full record

ArticleScientific reports2022

Personalized prediction of optimal water intake in adult population by blended use of machine learning and clinical data.

Alberto Dolci, Tiphaine Vanhaecke, Jiqiong Qiu, Riccardo Ceccato, Rosa Arboretti, Luigi Salmaso

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 45% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
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 at 2 institutions in 2 countries.

Alberto Dolci *Health, Hydration and Nutrition Science Department, Danone Research, Route Départementale 128, 91767, Palaiseau, France.
Tiphaine Vanhaecke *Health, Hydration and Nutrition Science Department, Danone Research, Route Départementale 128, 91767, Palaiseau, France. tiphaine.vanhaecke@danone.com.ORCID 0000-0001-9980-1657
Jiqiong QiuHealth, Hydration and Nutrition Science Department, Danone Research, Route Départementale 128, 91767, Palaiseau, France.
Riccardo CeccatoDepartment of Management and Engineering, University of Padova, Vicenza, Italy.
Rosa ArborettiDepartment of Civil, Environmental and Architectural Engineering, University of Padova, Padua, Italy.
Luigi SalmasoDepartment of Management and Engineering, University of Padova, Vicenza, Italy.
Danone (France) · FRUniversity of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growing evidence suggests that sustained concentrated urine contributes to chronic metabolic and kidney diseases. Recent results indicate that a daily urinary concentration of 500 mOsm/kg reflects optimal hydration. This study aims at providing personalized advice for daily water intake considering personal intrinsic (age, sex, height, weight) and extrinsic (food and fluid intakes) characteristics to achieve a target urine osmolality (U

Indexed as

DrinkingMachine LearningAdultAlgorithmsHumansNon-Randomized Controlled Trials as TopicNutrition PolicyOsmolar ConcentrationRandomized Controlled Trials as Topic

Identifiers

PMID36385111
PMCPMC9669042
OpenAlexW4309469232

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.