An innovative tool to prioritize the assessment of investigational COVID-19 therapeutics: A pilot project
Bibliographic record
Abstract
Background: As the COVID-19 pandemic unfolded, hundreds of investigational COVID-19 therapeutics emerged. Maintaining situational awareness of this extensive and rapidly evolving therapeutic landscape represented an unprecedented challenge for the Public Health Agency of Canada, as it worked to promote and protect the health of Canadians. A tool to triage and prioritize the assessment of these therapeutics was needed. Methods: The objective was to develop and conduct an initial validation of a tool to identify investigational COVID-19 therapeutics for further review based on an efficient preliminary assessment, using a systematic and reliable process that would be practical to validate, implement and update. Phase 1 of this pilot project consisted of a literature search to identify existing COVID-19 therapeutic assessment prioritization tools, development of the Rapid Scoring Tool (RST) and initial validation of the tool. Results: No tools designed to rank investigational COVID-19 therapeutics for the purpose of prioritizing their assessment were identified. However, a few publications provided criteria to consider and therapeutic ranking methods, which helped shape the development of the RST. The RST included eight criteria and several descriptors ("characteristics"). A universal characteristic scoring scale from -10 to 10 was developed. The sum of all the characteristic scores yielded an overall benefit score for each therapeutic. The RST appropriately ranked therapeutics using a systematic, reliable and practical approach. Conclusion: Phase 1 was successfully completed. The RST presents several distinct aspects compared with other tools, including its scoring scale and method, and capacity to factor in incomplete or pending information. It is anticipated that the framework used for the RST will lend itself to use in other dynamic situations involving many interventions.
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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 | 0.101 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".