P143 Difficulties in diagnosing tuberculosis during the COVID-19 pandemic – observational report from a tertiary care hospital in Mumbai (India)
Bibliographic record
Abstract
Introduction and Objective The COVID 19 pandemic caused significant disturbances in TB diagnostic and treatment services under national tuberculosis elimination programme (NTEP) in India. As India accounts for 28% of global burden, ending Tuberculosis globally is critically dependent on ending it in India. More than quarter of the world’s 10 million estimated cases and 449,700 of the world’s estimated 1.3 million TB related deaths occur in India. Between 2020 and 2025, 6 million TB cases and 1.4 million TB-related deaths are expected to occur in India. Due to lockdowns or movement restrictions, fear of contracting COVID-19 infection in hospital settings and diversion of TB services, patients with TB symptoms are having difficulty accessing healthcare facilities during this epidemic. The Objective of this study is to identify the real-world practical difficulties faced by TB patients during the COVID 19 pandemic during the second wave from March 2021 to October 2021 in India. Methods Figure 1. Results Out of 100 patients diagnosed with drug sensitive Tuberculosis, 42% were COVID-19 suspects.38% had symptoms for less than one month which helped in early diagnosis of Tuberculsosis.6% patients had symptoms for more than 6 months.27% patients faced problems getting diagnosed, out of which 14 patients (51.8%) had travel difficulty, 7 patients (25.9%) had financial problems and 6 patients (22.2%) had lack of health care access. The time taken for diagnosis and starting medication under National TB elimination program (NTEP) was 1–3 days in 47% patients, 4–7 days in 32% patients and 8 or more days in 21% patients. 31% of patients had side effects due to anti-tuberculosis treatment, amongst them 23 (74.1%) patients complained vomiting, 5 (16.1%) patients had itching, 3 patients (9.6%) had joint pains. 84% patients received regular supply of anti-tuberculosis medication and 16% faced problems in access.79% patients had access to high protein diet whereas 21% patients had no access. Conclusion This study highlights the consequences and impact of the COVID-19 pandemic on the Tuberculosis healthcare services. It highlights the problems faced during the COVID-19 lockdown by Tuberculosis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".