Chagas Disease Treatment Efficacy Biomarkers: Myths and Realities
Bibliographic record
Abstract
Chagas disease (CD), caused by Trypanosoma cruzi , affects millions of people worldwide. Although CD R&D has made progress during the last decade, clinicians and general practitioners are still facing the same challenge, i.e., the lack of adequate markers of clinical cure, hindering assessment of new drug efficacy in clinical trials and counseling of patients about treatment outcome. To date, no new markers have been validated as surrogates of seroreversion – the only marker of parasitological cure which is itself considered to be a surrogate of clinical benefit. T. cruzi DNA detected using PCR cannot currently be considered as a surrogate of seroconversion. Much emphasis has been placed on different T. cruzi antigens but no definite proof of correlation between titers, as determined by serology at a given timepoint, and seroreversion has been shown. Thanks to the improvement of analytical methods and the application of new methodologies, the identification of potential new markers is being facilitated, and some of these are progressing. However, there is a long journey from the identification of a potential biomarker to its clinical validation and acceptance by the regulatory authorities that requires a common effort from the entire Chagas community.
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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 | 0.005 | 0.006 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".