The challenging problem of disease staging in human African trypanosomiasis (sleeping sickness): a new approach to a circular question
Bibliographic record
Abstract
Human African trypanosomiasis (HAT), also known as sleeping sickness, puts millions of people at risk in sub-Saharan Africa and is a neglected parasitic disease that is almost always fatal if untreated or inadequately treated. HAT manifests itself in two stages that are difficult to distinguish clinically. The problem of staging in HAT is extremely important since treatment options, some of which are highly toxic, are directly linked to the disease stage. Several suggested investigations for disease staging have been problematic because of the lack of an existing gold standard with which to compare new clinical staging markers. The somewhat arbitrary current criteria based on the cerebrospinal fluid (CSF) white blood cell (WBC) count have been widely used, but the new potential biomarkers are generally compared with these, thereby making the problem somewhat circular in nature. We propose an alternative 'reverse' approach to address this problem, conceptualised as using appropriate statistical methods to test the performance of combinations of established laboratory variables as staging biomarkers to correlate with the CSF WBC/trypanosomes and clinical features of HAT. This approach could lead to the use of established laboratory staging markers, potentially leading to a gold standard for staging and clinical follow-up of HAT.
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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 | 0.194 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".