Establishing an international laboratory network for neglected tropical diseases: Understanding existing capacity in five WHO regions
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background.</ns4:bold> Limited laboratory capacity is a significant bottleneck in meeting global targets for the control and elimination of neglected tropical diseases (NTD). Laboratories are essential for providing clinical data and monitoring data about the status and changes in NTD prevalence, and for detecting early drug resistance. Currently NTD laboratory networks are informal and specialist laboratory expertise is not well publicised, making it difficult to share global expertise and provide training, supervision, and quality assurance for NTD diagnosis and research. This study aimed to identify laboratories within five World Health Organisation regions (South-East Asia, Eastern Mediterranean, Americas, Western Pacific and Europe) that provide NTD services and could be regarded as national or regional reference laboratories, and to conduct a survey to document their networks and capacity to support NTD programmes. </ns4:p> <ns4:p> <ns4:bold>Methods.</ns4:bold> Potential NTD reference laboratories were identified through systematic searches, snowball sampling and key informants. </ns4:p> <ns4:p> <ns4:bold>Results.</ns4:bold> Thirty-two laboratories responded to the survey. The laboratories covered 20 different NTDs and their main regional and national roles were to provide technical support and training, research, test validation and standard setting. Two thirds of the laboratories were based in academic institutions and almost half had less than 11 staff. Although greater than 90 per cent of the laboratories had adequate technical skills to function as an NTD reference laboratory, almost all laboratories lacked systems for external verification that their results met international standards. </ns4:p> <ns4:p> <ns4:bold>Conclusions.</ns4:bold> This study highlights that although many laboratories believed they could act as a reference laboratory, only a few had all the characteristics required to fulfil this role as they fell short in the standard and quality assurance of laboratory processes. Networks of high quality laboratories are essential for the control and elimination of disease and this study presents a critical first step in the development of such networks for NTDs. </ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 teacher head, 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".