IMPACT OF COVID 19 ON DETECTION OF TUBERCULOSIS AT A COMMUNITY LEVEL HEALTH CENTER IN ALIGARH, INDIA
Bibliographic record
Abstract
India is home to more than one quarter of the world's TB burden, and has reported 24.04 lakh new Tuberculosis cases in the year 2019. This number was before the pandemic struck whereas Tuberculosis notication rates fell by 25% during the period of January-June 2020 compared with the same period in 2019. To curb the spread of COVID-19, India announced a nationwide lockdown for 21 days beginning March 2020 that further was extended to several more weeks. This lockdown proved a major hurdle to Tuberculosis patients seeking health care and may result in a delay in diagnosis, treatment interruptions and disease transmission in household contacts. Lockdown has also forced many migrant workers to return to their homes, leading to treatment interruption. In addition to this, since Tuberculosis resembles symptoms of COVID19, this furthermore leads to hesitation and fear in care seeking behaviour leading to decreased Tuberculosis notication rates throughout the country.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".