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Record W4410547394 · doi:10.25259/ijn_385_2024

Approach to Diagnosis and Management of Pediatric Hypertension in an Outpatient Setting

2025· review· en· W4410547394 on OpenAlexaff
Shawn Khullar, Anu Asaithambi, Priya Pais, Rahul Chanchlani

Bibliographic record

VenueIndian Journal of Nephrology · 2025
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePsychological interventionIntervention (counseling)Intensive care medicineAdverse effectMedical diagnosisMEDLINEHealth professionalsBlood pressurePediatricsHealth careNursingPathologyInternal medicine

Abstract

fetched live from OpenAlex

Pediatric hypertension (HTN) is a public health concern with significant possible long-term adverse outcomes. This review is a comprehensive guide for pediatricians, nephrologists, and trainees, focusing on the latest approaches for HTN diagnoses in children and highlighting the importance of accurate blood pressure measurement techniques. We also explore current classification systems and offer evidence-based HTN management strategies tailored to pediatric patients. Lifestyle modifications are the recommended first-line interventions, including dietary changes, physical activity, and weight management. Pharmacological treatments are for severe cases or when lifestyle modifications are insufficient. The guidelines provide an overview of commonly prescribed antihypertensive medications, potential complications associated with untreated HTN, including target organ damage and increased cardiovascular risk in adulthood, and the importance of early recognition and intervention. This review aims to help healthcare professionals thoroughly understand pediatric HTN to improve diagnosis, treatment, and long-term outcomes.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.064
GPT teacher head0.324
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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