Medical research during the COVID-19 pandemic
Bibliographic record
Abstract
laboratory investigations, experimental animal models, or previous clinical experience in treating similar viruses such as SARS-CoV-1 or other retroviral infections. The running of any clinical trial during a pandemic is affected at multiple levels. Reasons for this include patient hesitancy or inability to continue investigative treatments due to self-isolation/quarantine, or limited access to public places (including hospitals). Additional barriers relate to health care professionals being committed to other critical tasks or quarantining themselves due to contact with COVID-19 positive patients. The best research approaches are those that adapt to such external unplanned obstacles. Ongoing clinical trials before COVID-19 pandemic have the potential for identifying important therapies in the long-term if they can be completed as planned. However, these clinical trials may require modifications due a pandemic such as this one to ensure the rights, safety, and wellbeing of participants as well as medical staff involved in the conduction of clinical trials. Clinical trials initiated during the pandemic must be time-efficient and flexible due to high contagiousness of severe acute respiratory syndrome coronavirus 2, the significant number of reported deaths, and time constraints needed to perform high quality clinical trials, enrolling adequate sample sizes. Collaboration between different countries as well as implementation of innovative clinical trial designs are essential to successfully complete such initiatives during the current pandemic. Studies looking at the long term sequalae of COVID-19 are also of importance as recent publications describe multi-organ involvement. Long term follow-up of COVID-19 survivors is thus also important to identify possible physical and mental health sequellae.
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 | 0.051 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".