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Record W4313475934 · doi:10.15690/vsp.v21i6.2495

Clinical Phenotypes of Malnutrition in Young Children: Differential Nutritional Correction

2023· article· en· W4313475934 on OpenAlexaff
I. А. Belyaeva, Еlena P. Bombardirova, Evgeniia A. Prihodko, Andrey Yu. Kruglyakov, А.А. Mikheeva, Arina R. Larina

Bibliographic record

VenueВопросы современной педиатрии · 2023
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsMalnutritionMedicinePediatricsEtiologyBronchopulmonary dysplasiaIntensive care medicineDifferential diagnosisInternal medicinePathologyPregnancyBiology

Abstract

fetched live from OpenAlex

This review article summarizes current data on malnutrition etiology and pathogenesis in infants. Topical requirements for revealing this condition, its diagnosis and severity assessment via centile metrics are presented. The characteristics of the most common clinical phenotypes of postnatal growth insufficiency in infants (premature infants with different degree of maturation, including patients with bronchopulmonary dysplasia) are described. Differential approaches for malnutrition nutritional correction in these children are presented. The final section of the article describes special nutritional needs for children with congenital heart defects in terms of hemodynamic disorders nature and severity. Modern nutritional strategies for preparation of these patients to surgery and for their postoperative period are presented. The use of high-calorie/high-protein product for malnutrition correction in the most vulnerable patients with described in this review phenotypes is worth noticing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.341
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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