A simple validated prediction rule to diagnose typhoid fever in Turkey
Bibliographic record
Abstract
The goal of this study was to develop a simple prediction rule for the diagnosis of typhoid fever. A model for the prediction of patients with typhoid fever at hospital admission was derived and validated by assigning weighted point values to independent predictive factors associated with a diagnosis of typhoid fever at hospital admission. Patient demographic, clinical and laboratory variables were used to compare patients with blood culture-confirmed typhoid fever with patients with fever of unknown origin. The model was derived and validated in two separate cohorts of patients from Dicle University Hospital in Diyarbakir, Turkey. A total of 371 patients were enrolled. A diagnostic index score was created using seven independent predictive factors associated with typhoid fever at hospital admission: age <30 years, abdominal distention, confusion, leukopenia, relative bradycardia, positive Widal test and a typhoid tongue. A clinical prediction rule helped to differentiate patients with typhoid fever.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".