Retrospective Cohort Study of Effects of the COVID-19 Pandemic on Tuberculosis Notifications, Vietnam, 2020
Bibliographic record
Abstract
severe acute respiratory syn- drome coronavirus 2 (SARS-CoV-2) has been causing a global coronavirus disease pandemic that has had wide-reaching effects on delivery of care for many other health conditions, including tuberculosis (TB). Each year, 10 million TB cases are diagnosed and 1.5 million TB deaths occur worldwide (1). The World Health Organization (WHO) has identifi ed substantial effects of the COVID-19 pandemic on TB control efforts (1). By late 2020, substantial reductions in TB case notifi cations were evident in both high-and middle-income countries (2-6), including countries where COVID-19 had been well-controlled (7). Decreased TB notifi cations led to fears that delays in case detection and reduced treatment completion resulting from the COVID-19 pandemic might lead to increased Mycobacterium tuberculosis transmission and consequently higher mortality rates (8). Indeed, evidence suggests that the COVID-19 pandemic has resulted in reduced patient adherence to treatment (9), decreased access to medications (10,11), delayed access to services (10,12), and higher rates of loss to follow-up for patients with TB (10). Some of this disruption has been attributed to diversion of resources and interruptions to drug supply and delivery resulting from the COVID-19 pandemic (13). Furthermore, some persons with TB have avoided seeking healthcare because of fear of acquiring . In addition, evidence from South Africa suggests that outcomes for SARS-CoV-2 infection are worse for patients co-infected with TB (15).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".