Tropical fever in remote tropics: tuberculosis or melioidosis, it depends on the lab
Bibliographic record
Abstract
Diagnostics tests used to identify the cause of infection using proteomics and genomics have revolutionised microbiology laboratories in recent times. However, approaches to build the capacity of clinical microbiology services in the rural tropics by simply transplanting these approaches have proven difficult to sustain. Tropical fever in the remote tropics is, by definition, a clinical diagnosis where the aetiology of fever is not known, treatment is empirical, guided by clinical suspicion with treatment failure often attributed to incorrect diagnosis or antimicrobial resistance. Tuberculosis (TB) in rural Papua New Guinea (PNG) is mostly diagnosed clinically, perhaps supported by microscopy. In fact, a ‘tuberculosis patient’ in rural PNG is included in the TB register upon commencement of TB treatment with or without any laboratory-based evidence of infection. The roll-out of GeneXpert is continuing to transform TB diagnostic certainty in TB endemic communities. Melioidosis is endemic in tropical regions and is increasingly reported to mimic TB. Isolation and identification of the causative agent Burkholderia pseudomallei remains the gold standard. Here, we discuss the increasing divide between rural and urban approaches to laboratory-based infection diagnosis using these two enigmatic tropical infectious diseases, in rural PNG, as examples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".