Physician deaths from corona virus (COVID-19) disease
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers. AIMS: We tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events. METHODS: On 15 April 2020, a Google internet search was performed using the keywords 'doctor', 'physician', 'death', 'COVID' and 'coronavirus' in English and Farsi, and Chinese using the Baidu search engine. The age, sex and medical speciality of physicians who died from COVID-19 in the line of duty were recorded. Individuals greater than 90 years of age were excluded. RESULTS: We found 278 physicians who died with COVID-19 infection, but complete details were missing for 108 individuals. The average age of the physicians was 63.7 years with a median age of 66 years, and 90% were male (235/261). General practitioners and emergency room doctors (108/254), respirologists (5/254), internal medicine specialists (13/254) and anaesthesiologists (6/254) comprised 52% of those dying. Two per cent of the deceased were epidemiologists (5/254), 2% were infectious disease specialists (4/254), 6% were dentists (16/254), 4% were ENT (9/254) and 3% were ophthalmologists (8/254). The countries with the most reported physician deaths were Italy (121/278; 44%), Iran (43/278; 15%), Philippines (21/278; 8%), Indonesia (17/278; 6%), China (16/278; 6%), Spain (12/278; 4%), USA (12/278; 4%) and UK (11/278;4%). CONCLUSIONS: Physicians from all specialities may die from COVID. Lack of personal protective equipment was cited as a common cause of death. Consideration should be made to exclude older physicians from front-line work.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".