A Phase III Diagnostic Accuracy Study of a Rapid Diagnostic Test for Diagnosis of Second-Stage Human African Trypanosomiasis in the Democratic Republic of the Congo
Bibliographic record
Abstract
OBJECTIVES: To estimate the diagnostic accuracy of HAT Sero K-SeT for the field diagnosis of second-stage human African trypanosomiasis (HAT). DESIGN: A phase III diagnostic accuracy design. Consecutive patients with symptoms clinically suggestive of HAT were prospectively enrolled. We compared results of the index test HAT Sero K-SeT with those of a composite reference standard: demonstration of trypanosomes in cerebrospinal fluid (CSF), or trypanosomes detected in any other body fluid AND white blood cell count in CSF >5/μl. SETTING: Rural hospital in the Democratic Republic of the Congo. PARTICIPANTS: All patients above five years old presenting at Mosango hospital with a neurological problem of recent onset at the exclusion of trauma. INTERVENTIONS: n.a. MAIN OUTCOME MEASURES: Sensitivity and specificity of HAT Sero K-SeT test. RESULTS: The sensitivity of the HAT Sero K-SeT was 8/8 or 100.0% (95% confidence interval: 67.6 to 100.0%) and the specificity was 258/266 or 97.0% (94.2% to 98.5%). CONCLUSION: The high sensitivity of the HAT Sero K-SeT is in line with previously published estimates, though the sample of HAT cases in this study was small. The specificity estimate was very high and precise. This test, when negative, allows the clinician to rule out HAT in a clinical suspect in a hospital setting in this endemic region.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".