Rapid Diagnostic Tests for Neglected Infectious Diseases: Case Study Highlights Need for Customer Awareness and Postmarket Surveillance
Bibliographic record
Abstract
Accurate diagnosis of infectious diseases is essential for appropriate targeting of treatment and disease control. Rapid diagnostic tests (RDTs) are quick and easy to perform, they give results during one clinic visit, and they can be used in settings with little infrastructure or trained personnel. RDTs are promising tools to improve diagnosis in remote or low-resource settings. In the field of neglected infectious diseases, new manufacturers, RDTs, and users are coming onto the scene [1, 2]. As access to RDTs improves, the need for quality assurance and postmarket surveillance increases. The International Medical Device Regulators Forum has formulated guidelines about quality assurance of medical devices, including RDTs, which have been adopted as regulatory standards in Australia, Canada, the European Union, Japan, and the United States [3]. Specific quality standards for in vitro diagnostic tests (IVDs) (ISO 13485:2003) and medical laboratories (ISO 15189:2012) have been published by the International Organisation for Standardisation [4, 5]. In less-regulated settings, the World Health Organisation (WHO) has stepped in to promote IVD quality [3, 6, 7]. Participation from various stakeholders is required to assure RDT quality. Manufacturers must ensure that their products are ready for the market—i.e., that product design, development, testing, manufacturing, packaging, and labelling meet the required standards of safety and performance. The role of RDT users is, among other responsibilities, to know indications, contraindications, and operating procedures of the devices. Most regulatory authorities recognise that efficient communication between manufacturers and users is key to postmarket surveillance [3–7]. In low-resource settings and in the field of neglected infectious diseases, this communication between manufacturers and users may be suboptimal, as well as the pre- and postmarketing oversight of national regulatory authorities. The Neglected Infectious Diseases dIAGnosis (NIDIAG) consortium aims to improve diagnostic approaches for different clinical syndromes in low-resource settings where neglected infectious diseases are prevalent. In this case study, we assessed several quality aspects of RDTs used in the NIDIAG study about persistent fever: we focused on RDT labelling and instructions for use (IFU) and on product-related incidents, including communication with manufacturers about these incidents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".