COVID-19 research in critical care: the good, the bad, and the ugly
Bibliographic record
Abstract
The extraordinary pace of research on coronavirus disease 2019 (COVID-19) has been one of the major success stories of the pandemic. Therapeutic trials involving thousands of patients, which usually take years to complete, have been reported in a matter of months. National and international registries and networks have reported on tens of thousands of patients in near real time. However, there have also been many challenges: hundreds of trials have been underpowered, duplicated, or of poor quality; excessive bureaucracy has complicated study initiation; and only a small percentage of eligible patients worldwide have been enrolled in studies, while many others have been treated with off-label, unproven therapies. All of this has been complicated by an “infodemic” of low-quality medical information, accelerated by social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".