Evidence map›Paper›PMID 24470871›Full record

ArticleIranian journal of basic medical sciences2013

Designing a Human T-Lymphotropic Virus Type 1 (HTLV-I) Diagnostic Model using the Complete Blood Count.

Masoumeh Sarbaz, Omid Pournik, Leila Ghalichi, Khalil Kimiafar, Amir Reza Razavi

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Iranian journal of basic medical sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

5 authors.

Masoumeh SarbazDepartment of Medical Informatics, School of Medicine and Faculty Member of Health Information Technology and Medical Records Department, School of Paramedical Sciences, Mashhad Univer-sity of Medical Sciences, Mashhad, Iran.
Omid PournikDepartment of Medical informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran and Deputy for Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Leila GhalichiDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Khalil KimiafarDepartment of Health Information Technology and Medical Records, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran and Department of Health Information Management, Faculty of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, I.R. Iran.
Amir Reza RazaviDepartment of Medical informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

OBJECTIVE(S): Infection caused by Human T-Lymphotropic Virus Type 1 (HTLV-I) can be observed in some areas of Iran in form of endemic. Most of the cases are asymptomatic, and few cases progress to malignancies and neural diseases. Designing and implementing a model to screen people especially in endemic regions can help timely detection of infected people and improve the prognosis of the disease. MATERIALS AND

methodsIn this study, results of the complete blood count (CBC-diff) for 599 healthy people and the patients with different types of Leukemia and HTLV-I have been examined. Modeling was made using CHAID method. The final model was carried out based on the number of white blood cells (WBC), platelets, and percentages of eosinophils.

resultsThe accuracy of the final model was 91%. By applying this model to the CBC-diff results of people without symptoms or miscellaneous patients in endemic regions of our country, disease carriers can be identified and referred for supplementary tests.

conclusionWith regard to the prevalence of different complications in infected people, these individuals can be identified earlier, leading to the improvement of the prognosis of this disease and the increase of the health status especially in endemic regions.

Indexed as

CHAIDDecision TreeHTLV-IMachine Learning

Identifiers

PMID24470871
PMCPMC3881245

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.