Development of a clinical scoring system to make a presumptive diagnosis of Kyasanur Forest Disease: a case-control study from South India
Bibliographic record
Abstract
Introduction: Kyasanur Forest Disease (KFD) is a viral haemorrhagic fever endemic in South India. Based on clinical presentation alone, it is challenging to distinguish KFD from other febrile illnesses in the region. The study aimed to develop a clinical scoring system for early presumptive diagnosis of KFD. Patients and methods: This retrospective case-control study included microbiologically diagnosed KFD patients (n=186) with other undifferentiated febrile illnesses as controls (n=203). The clinical and laboratory features between cases and controls were compared. A logistic regression analysis included those variables found to be significantly associated with KFD on univariate analysis. The adjusted odds ratio for the significant variables was calculated and converted into logarithmic scales. These numbers were rounded off to the nearest integer to find the score assigned to each variable. A receiver operating characteristics curve was created to find the best cut-off for the scoring system that predicted the diagnosis of KFD. Results: A total of 186 anonymised cases and 203 anonymised controls were recruited from the records for this study. Myalgia, headache, lymphadenopathy, bleeding manifestations, Central Nervous System (CNS) involvement, raised haematocrit, leukopenia, and raised transaminases were more common in patients with KFD. Except for lymphadenopathy and raised transaminases, all the other variables were independent predictors of making a diagnosis of KFD. Since raised transaminases tended towards significance, it was included in the scoring system with other independent predictors. A scoring system was created with a maximum score of 12. The receiver operating characteristic curve showed an Area Under Curve of 0.912 (95%CI: 0.88-0.94). A score of 4 or more was found to have a sensitivity and specificity of 83% and 87%, respectively. Conclusion: The presence of specific features should alert primary care physicians working in endemic areas about the possibility of KFD. This diagnostic scoring system can be used to make a presumptive diagnosis of KFD after undergoing a prospective validation study.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".