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Record W4313412159 · doi:10.7759/cureus.33027

Predictive Factors for the Complications of Dengue Fever in Children: A Retrospective Analysis

2022· article· en· W4313412159 on OpenAlexaff
Nachappa Sivanesan Uthraraj, Laya Manasa Sriraam, Meghanaprakash Hiriyur Prakash, Manoj Kumar, Uthraraj Palanisamy, Kannaki Uthraraj Chettiakkapalayam Venkatachalam

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineAscitesDengue feverInternal medicinePopulationPlateletMedical recordImmunology

Abstract

fetched live from OpenAlex

Background and objective Dengue fever (DF) and its complications - dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) - are major public health problems in Southeast Asia. Predicting the development of DHF and DSS using hematological parameters and ultrasonic signs of vascular leakage will help in reducing morbidity and mortality associated with these diseases. Hence, this study aimed to test the association of platelets and packed cell volume (PCV) on day one (D1) of admission with gallbladder wall thickness (GWT) and ascites, which herald the onset of DHF and DSS. Methods The electronic health records of 52 pediatric patients admitted during a mini-outbreak were analyzed to assess platelets and PCV on D1, laboratory and ultrasonography findings, and outcomes. Correlations between D1 hematological parameters and GWT and ascites were tested. Results There was a positive correlation between GWT of more than 5 mm and ascites. However, there was no significant correlation of platelets and PCV on D1 with either GWT or ascites and consequently DHF or DSS. All the patients responded to fluid, blood, and supportive therapy. There were no mortalities. Conclusion Patients who develop GWT after DF are at an increased risk of developing ascites that deteriorate to DHF and DSS. D1 platelets and PCV are not reliable indicators for predicting the progression or worsening of the disease in the pediatric population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 teacher head, 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

Citations8
Published2022
Admission routes1
Has abstractyes

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