Lessons Learned from Coronavirus Disease 2019 (COVID-19) Therapies: Critical Perspectives From the Infectious Diseases Society of America (IDSA) COVID-19 Treatment Guideline Panel
Bibliographic record
Abstract
Despite the challenges of the pandemic, there has been substantial progress with coronavirus disease 2019 (COVID-19) therapies. Pivotal COVID-19 trials like SOLIDARITY, RECOVERY, and ACCT-1 were rapidly conducted and data disseminated to support effective therapies. However, critical shortcomings remain on trial conduct, dissemination and interpretation of study results, and regulatory guidance in pandemic settings. The lessons that we learned have implications for both the current pandemic and future emerging infectious diseases. There is a need for establishing and standardizing clinical meaningful outcomes in therapeutic trials and for targeting defined populations and phenotypes that will most benefit from specific therapies. Standardized processes should be established for rapid and critical data review and dissemination to ensure scientific integrity. Clarity around the evidence standards needed for issuance of both emergency use authorization (EUA) and biologic license application (BLA) should be established and an infrastructure for executing rapid trials in epidemic settings maintained.
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 | 0.148 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.018 | 0.051 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".