Implementation of a Triage Protocol Outside the Hospital Setting for Timely Referral During the COVID-19 Second Wave in Chennai, India
Bibliographic record
Abstract
India experienced a surge in COVID-19 cases during the second wave in the period of April-June 2021. A rapid rise in cases posed challenges to triaging patients in hospital settings. Chennai, the fourth largest metropolitan city in India with an 8 million population, reported 7564 COVID-19 cases on May 12, 2021, nearly 3 times higher than the number of cases in the peak of COVID-19 in 2020. A sudden surge of cases overwhelmed the health system. We had established standalone triage centers outside the hospitals in the first wave, which catered to up to 2500 patients per day. In addition, we implemented a home-based triage protocol from May 26, 2021, to evaluate patients with COVID-19 who were aged ≤45 years without comorbidities. Among the 27,816 reported cases between May 26 and June 24, 2021, a total of 16,022 (57.6%) were aged ≤45 years without comorbidities. The field teams triaged 15,334 (55.1%), and 10,917 (39.2%) patients were evaluated at triage centers. Among 27,816 cases, 19,219 (69.1%) were advised to self-isolate at home, 3290 (11.8%) were admitted to COVID-19 care centers, and 1714 (6.2%) were admitted to hospitals. Only 3513 (12.7%) patients opted for the facility of their choice. We implemented a scalable triage strategy covering nearly 90% of the patients in a large metropolitan city during the COVID-19 surge. The process enabled early referral of high-risk patients and ensured evidence-informed treatment. We believe that the out-of-hospital triage strategy can be rapidly implemented in low-resource settings.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".