Assessment and initial management of acute undifferentiated fever in tropical and subtropical regions
Bibliographic record
Abstract
### What you need to know Acute undifferentiated febrile illnesses (AUFI) are characterised by fever of less than two weeks’ duration without organ-specific symptoms at the onset.1 These may begin with headache, chills, and myalgia. Later, specific organs may be involved. AUFIs can range from mild and self limiting disease to progressive, life threatening illness. A mortality rate of 12% has been reported in severely ill hospitalised patients in tropical regions.2 AUFIs are classified into malaria and non-malarial illnesses with the help of microscopy or rapid diagnostic tests for malaria.3 The overlap of epidemiological and clinical features often renders clinical diagnosis difficult. There is greater focus on non-malarial AUFIs with the decline of malaria in many regions of the world.4 They account for 20-50% of all fevers in children over 5 years of age and adults in Asia and Africa.5 Laboratory confirmation is difficult—in contrast to malaria and dengue, for which high accuracy rapid diagnostic tests are now available. Current guidelines do not comprehensively address undifferentiated infections, which can fuel indiscriminate use of antimalarials …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".