NS1: An early Diagnostic Tool for Dengue Virus Fever
Bibliographic record
Abstract
Dengue virus is a highly prevailing unremitting pathological menace in the developing countries like Pakistan. The variation in the type and frequency of dengue infection demands a continuous surveillance on its spread and diagnosis, in order to develop appropriate management and therapeutic strategies. Dengue Virus is highly complex disease with various manifestations. It is hard to characterize the dengue viral infection with ordinary laboratory tests. However, NS1 could serve a good diagnostic tool in the first few days of fever. In this research, 88 patients from Lahore regions of Punjab were analysed for the presence of dengue specific NS1 antigen from 1 to 30 days of dengue fever. ELISA based kit was employed. The obtained data was analysed expressed percentage frequency of NS1 positive and negative along with IgM positive and negative in all dengue fever patients. Importantly it was observed that 36.3 % were infected by dengue. NS1 antigen was efficiently quantified at earlier days of infection. IgM was negative in all the subjects who were positive for NS1 on the second day of fever. However, this association weakened with the progression of fever. It was analysed that thrombocytopenia and leuckocytopenia are not linked with the NS1 positivity and these states were random. It can be inferred that dengue diagnosis is very complicated and the antigenic profile dramatically changes with the progression of fever in terms of days. Clinicians should employ a diagnostic method based on a comprehensive analysis of subjects in order to minimize
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".