A unified citywide dashboard for allocation and scheduling dialysis for COVID-19 patients on maintenance hemodialysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused significant global disruption, especially for chronic care like hemodialysis treatments. Approximately 10,000 end-stage kidney disease (ESKD) patients are receiving maintenance hemodialysis (MHD) at 174 dialysis centers in Greater Mumbai. Because of the fear of transmission of infection and inability to isolate patients in dialysis centers, chronic hemodialysis care was disrupted for COVID-19-infected patients. Hence, we embarked on a citywide initiative to ensure uninterrupted dialysis for these patients. Materials and Methods: The Municipal Corporation of Greater Mumbai (MCGM) designated 23 hemodialysis facilities as COVID-positive centers, two as COVID-suspect centers, and the rest continued as COVID-negative centers to avoid transmission of infection and continuation of chronic hemodialysis treatment. Nephrologists and engineers of the city developed a web-based-portal so that information about the availability of dialysis slots for COVID-infected patients was easily available in real time to all those providing care to chronic hemodialysis patients. Results: The portal became operational on May 20, 2020, and as of December 31, 2020, has enrolled 1,418 COVID-positive ESKD patients. This initiative has helped 97% of enrolled COVID-infected ESKD patients to secure a dialysis slot within 48 hours. The portal also tracked outcomes and as of December 31, 2020, 370 (27%) patients died, 960 patients recovered, and 88 patients still had an active infection. Conclusions: The portal aided the timely and smooth transfer of COVID-19-positive ESKD patients to designated facilities, thus averting mortality arising from delayed or denied dialysis. Additionally, the portal also documented the natural history of the COVID-19 pandemic in the city and provided information on the overall incidence and outcomes. This aided the city administration in the projected resource needs to handle the pandemic.
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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 | 0.002 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it