The burden of COVID-19 pandemic on tuberculosis detection: a single-center study
Bibliographic record
Abstract
Abstract Background Since being declared a global pandemic, Coronavirus disease 2019 (COVID-19) took over healthcare providers and researchers’ interest. However, other epidemic diseases, including tuberculosis (TB), are still a health issue that aggravate under the umbrella of health facilities exhaustion. This study aims to evaluate the impact of the COVID-19 pandemic on tuberculosis management. Methods A retrospective analysis of the quarterly reports issued by a tuberculosis management unit from 2017 to June 2021, including data of 12 subunits. The changes in pulmonary and extrapulmonary tuberculosis incidence trends (new + relapsed cases) throughout the 4 years were reported. The quarterly changed percentages in cases numbers along 2020 and first half of 2021 was compared with that of the same periods in 2019. Results Incidence of extrapulmonary tuberculosis was higher than pulmonary tuberculosis throughout the 4 year study periods (7.69 vs. 4.49, 9.44 vs. 4.33, 7.75 vs. 3.58, and 7.82 vs. 2.94/100.000 population, respectively) with a noticeable decline in the incidence of pulmonary TB during 2020. The second quarter of 2020 showed the lowest tuberculosis incidence rate with a 41.6% decline in the total number of diagnosed cases while 2nd quarter of 2021 showed 21.2% decline. During 2020, only 4 cases of multidrug-resistant TB were reported (compared to an average of 8 cases of MDR-TB yearly before the COVID-19 era). Conclusion There was a noticeable drop in tuberculosis case detection during the COVID-19 pandemic. The lockdown, started in Egypt by the end of March 2020, could contribute to the marked drop in the second quarter. However, a steady partial decline was continued during the first half of 2021, which foretells a growing problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".