Lower Incidence of COVID-19 at High Altitude: Facts and Confounders
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
. 21:217-222, 2020.-The rapid transmission, increased morbidity, and mortality of coronavirus disease 2019 (COVID-19) has exhausted many health care systems and the global economy. Large variations in COVID-19 prevalence and incidence have been reported across and within many countries worldwide; however, this remains poorly understood. The variability and susceptibility across the world have been mainly attributed to differing socioeconomic status, burden of chronic diseases, access to health care, strength of health care systems, and early or late adoption of control measures. Environmental factors such as pollution, ambient temperature, humidity, and seasonal weather patterns at different latitudes may influence how severe the pandemic is and the incidence of infection in any part of the world. In addition, recent epidemiological data have been used to propose that altitude of residence may not only influence those environmental features considered key to lesser viral transmission, but also susceptibility to more severe forms of COVID-19 through hypoxic-hypobaria driven genomic or nongenomic adaptations specific to high-altitude populations. In this review, we critically examine these factors and attempt to determine based upon available scientific and epidemiological data whether living in high-altitude regions might be protective against COVID-19 as recent publications have claimed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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