COVID-19 and Sudden Unexpected Community Deaths in Lusaka, Zambia, Africa - A Medico-Legal Whole-Body Autopsy Case Series
Bibliographic record
Abstract
BACKGROUND: Data from Africa regarding sudden and unexpected COVID-19 community deaths and underlying pathological, demographic, and co-morbidity features require definition. METHODS: We performed a case series of COVID-19-related deaths seen at Forensic Post-Mortem examination of sudden and unexpected Community Deaths in Lusaka, Zambia, Africa. Whole-body Post-Mortem examinations were performed according to Standard Operating Procedures. Patient demographics, history, co-morbidities, pathological gross and microscopic findings, and cause(s) of death were recorded. Variables were grouped as frequencies and percentages. Comparison of data was made with autopsy findings of hospital COVID-19 deaths. FINDINGS: Of 21 COVID-19 decedents, 14/21 (66.7%) were male; 18/21, (85.7%) were below 55 years of age (mean age, 40 ± 12.3; range, 20-73). The median duration of symptoms was 1 day (range 0-2); 9/21 (42.9%) had co-morbidities, with hypertension and obesity being the most common. Main post-mortem findings were diffuse alveolar damage (DAD) (80.9%), saddle and shower emboli (38.1%, respectively), and pneumonia (14.3%). Pulmonary thromboembolism (76.2%), DAD (14.3%), and SARS-CoV-2 pneumonia (9.5%) were common causes of death. CONCLUSIONS: COVID-19 is an important cause of death to consider in forensic investigations of sudden and unexpected community deaths. Risk factors for the younger age of COVID-19 deaths and thromboembolism need to be identified.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".