Time required to initiate outbreak and pandemic observational research
Bibliographic record
Abstract
PURPOSE: Observational research focused upon emerging infectious diseases such as Ebola virus, Middle East respiratory syndrome, and Zika virus has been challenging to quickly initiate. We aimed to determine the duration of start-up procedures and barriers encountered for an observational study focused upon such infectious outbreaks. MATERIALS AND METHODS: At 1 pediatric and 5 adult intensive care units, we measured durations from protocol receipt to a variety of outbreak research milestones, including research ethics board (REB) approval, data sharing agreement (DSA) execution, and patient study screening initiation. RESULTS: The median (interquartile range) time from site receipt of the protocol to REB submission was 73 (30-126) days; to REB approval, 158 (42-188) days; to DSA completion, 276 (186-312) days; and to study screening initiation, 293 (269-391) days. The median time from REB submission to REB approval was 43 (13-85) days. The median time for all start-up procedures was 335 (188-335) days. CONCLUSIONS: There is a lengthy start-up period required for outbreak-focused research. Completing DSAs was the most time-consuming step. A reactive approach to newly emerging threats such as Ebola virus, Middle East respiratory syndrome, and Zika virus will likely not allow sufficient time to initiate research before most outbreaks are advanced.
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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 | 0.109 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".