Evaluation of laboratory workers competency on loop mediated isothermal amplification (LAMP) technology for malaria diagnostics: a technology transfer assessment study (LAMP-TTA) during a multi-site clinical trial
Bibliographic record
Abstract
BACKGROUND: Molecular malaria diagnostic approaches present high sensitivity and detect submicroscopic parasitaemia in a patient. However, molecular techniques like polymerase chain reaction are costly, require specific equipment and the reaction read-out interpretation can require extensive experience. Hence, a simple and user-friendly loop mediated isothermal amplification molecular technique resolves limitations of the polymerase chain reaction. However, performing accurate and high-quality loop mediate isothermal amplification reactions requires appropriate training and follow up, particularly for laboratory staff who have never performed molecular methods. This study aimed to assess the potential gain in knowledge and technical skills provided by the initial training and during onsite support visits. METHODS: All laboratory technicians hired in the LAMPREG study were enrolled, consented and skills were evaluated using a questionnaire before and after training, as refresher and follow up interventions during health centre inspections. Data entry was performed using an excel table. Data analysis was performed using Prism Graphpad v8.0 for windows. RESULTS: From the total enrolled 15 participants, all but one participant reported no previous work experience with LAMP technology. In contrast to pre-initial training evaluations, a report during post-training showed the self-assessed technical knowledge median rating increased by four points (median score of 5/5, range 4/5-5/5), and knowledge of the LAMP chemistry itself increased by 9 points (median score of 10/10, range 6/10-10/10). Regarding assessment on follow up visits, all participants reported being self confident on their ability to run a LAMP reaction, and the self-assessment of raw results interpretation was excellent with a median score of 10/10 (range 8/10-10/10) at the first follow-up and 10/10 (range 10/10-10/10) at the second follow up assessment. CONCLUSION: Clinical trials and diagnostic studies lead to the new implementation of technology in rural settings. Providing adequate training, support, and evaluation, is part of a successful technology transfer programme.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".