Bibliographic record
Abstract
Background: Symptomatic dengue infection can be classified into 3 patterns based on their severity; dengue fever (DF ) , dengue hemorrhagic fever (DHF ) and dengue shock syndrome (DSS). In clinical practice, the diagnosis and management are based on clinical findings and abnormal initial laboratory tests . We conducted the present study to explore a set of parameters, preferable routine, or at least easy to investigate, that could be used as indicators for dengue infection severity . Methods: A retrospective cohort study was conducted in three university-affiliated hospitals, one general hospital and two referral hospitals, in the northern part of Thailand. Patients were grouped into the three severity categories (DF, DHF, and DSS), using modified WHO criteria. Pre-defined prognostic indicators were compared. The prognostic indicators for dengue severity were analyzed by a multivariable polytomous logistic regression and presented with odds ratios. Results: From 777 patients overall, 391 were classified as DF, 296 with DHF, and 90 with DSS. The characteristics that increased the risk of DHF were; age > 6 year (OR = 1.85), hepatomegaly (OR = 3.49), any bleeding episodes (OR = 1.43), white cell count > 5,000/µL (OR = 1.80), and platelet <= 100,000/µL (OR = 3.72). The characteristics that increase the risk of DSS were; hepatomegaly (OR = 43.44), any bleeding episodes (OR = 5.58), pulse pressure <= 20 mmHg (OR = 19.09), SBP < 90 mmHg (OR = 2.45), hematocrit > 40% (OR = 1.88), white cell count > 5,000/µL (OR = 2.36), and platelet <= 100,000/µL (OR = 10.60). Conclusions: The severity of dengue infection is significantly associated with some routine clinical parameters. These parameters may be used to develop a future scoring system to predict and manage dengue infection severity early in the course of the illness. doi: http://dx.doi.org/10.4021/ijcp73w
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".