Enrollment of dengue patients in a prospective cohort study in Umphang District, Thailand, during the COVID‐19 pandemic: Implications for research and policy
Bibliographic record
Abstract
Background and Aims: Dengue is endemic in Thailand and imposes a high burden on the health system and society. We conducted a prospective cohort study in Umphang District, Tak Province, Thailand, to investigate the share of dengue cases with long symptoms and their duration. Here we present the results of the enrollment process during the COVID-19 pandemic with implications and challenges for research and policy. Methods: In a prospective cohort study conducted in Umphang District, Thailand, we examined the prevalence of persistent symptoms in dengue cases. Clinically diagnosed cases were offered free laboratory testing, We enrolled ambulatory dengue patients regardless of age who were confirmed through a highly sensitive laboratory strategy (positive NS1 and/or IgM), agreed to follow-up visits, and gave informed consent. We used multivariate logistic regressions to assess the probability of clinical dengue being laboratory confirmed. To determine the factors associated with study enrollment, we analyzed the relationship of patient characteristics and month of screening to the likelihood of participation. To identify underrepresented groups, we compared the enrolled cohort to external data sources. Results: The 150 clinical cases ranged from 1 to 85 years old. Most clinical cases (78%) were confirmed by a positive laboratory test, but only 19% of those confirmed enrolled in the cohort study. Women, who were half as likely to enroll as men, were underrepresented in the cohort. Conclusions: The Thai physicians' clinical diagnoses at this rural district hospital had good agreement with laboratory diagnoses. By identifying underrepresented groups and disparities, future studies can ensure the creation of statistically representative cohorts to maximize their scientific value. This involves recruiting and retaining underrepresented groups in health research, such as women in this study. Promising strategies for meaningful inclusion include multi-site enrollment, offering in-home or virtual services, and providing in-kind benefits like childcare for underrepresented groups.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".