Evidence map›Paper›PMID 32952579›Full record

ReviewIranian journal of child neurology2020

Approach to Patients with Neurometabolic Diseases Who Show Characteristic Signs and Symptoms.

Parvaneh Karimzadeh, Mohammad Ghofrani, Shahram Nasiri

Abstract readReview
In one paragraph

Review in Iranian journal of child neurology, 2020. 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
0.3field-weighted citation impact, top 44% 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, 3 citations in OpenAlex.

  1. Development and clinical translation ofComputational and structural biotechnology journal · 2022
    Review
  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

3 authors at 2 institutions in 1 country.

Parvaneh KarimzadehDepartment of Pediatric Neurology, Pediatric Neurology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad GhofraniDepartment of Pediatric Neurology, Pediatric Neurology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Shahram NasiriDepartment of Paediatric Neurology, Abuzar children's medical center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranCorresponding Author Nasiri SH. MD.
Children's Medical Center · IRResearch Institute for Endocrine Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurometabolic disorders are hereditary conditions mainly affect the function of the brain and the nervous system. The prevalence of these disorders is 1 in 1,000 live births. Such disorders, at different ages, could manifest as sepsis, hypoglycemia, and other neurologic disorders. Having similar manifestations leads to delayed diagnosis of neurometabolic disorders. A number of neurometabolic disorders have known treatments; however, to prevent long-term complications the key factors are early diagnosis and treatment. Although a large number of neurometabolic diseases have no treatment or cure, the correct and on-time diagnosis before death is important for parents to have plans for prenatal diagnosis. Different diagnostic procedures could be offered to parents, enzymatic procedures, and determining metabolites in plasma, urine, and CSF, and molecular genetic diagnosis. Molecular genetic diagnostic procedures are expensive and could not be offered to all parents. Therefore, we aimed to design algorithms to diagnose neurometabolic disorders according to some frequent and characteristic signs and symptoms. By designing these algorithms and using them properly, we could offer diagnostic enzymatic panels. These enzymatic panels are inexpensive; thereby reducing the financial burden on the parents. Also, having an early diagnosis according to these panels could lead to offering more accurate and less expensive molecular genetic tests.

Indexed as

algorithmschildrendiagnosisenzymatic panelsNeurometabolic disorders

Identifiers

PMID32952579
PMCPMC7468080
OpenAlexW3087098938

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.