Evaluating the effects of a global pandemic on the operation of an investigational drug service
Bibliographic record
Abstract
PURPOSE: This study is an analysis of the changes to workload and operations of UNC Health's investigational drug service (IDS) brought about by the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Workload statistics were collected and analyzed for trend changes to illustrate operational changes necessitated by the COVID-19 pandemic within the IDS pharmacy at UNC Health. RESULTS: Multiple workload metrics declined at the beginning of the COVID-19 pandemic, followed by an increase in the metrics for many categories as the pandemic continued. Notably, monthly inventory added initially decreased by 37.5%, later leveling off but showing increased variability. Fills dispensed and monitoring visits both decreased by 34.5% from the first quarter (Q1) to Q2 of 2020. Both metrics returned to or slightly exceeded prepandemic levels by the end of the study period in March 2021. Patient enrollment decreased 76% from February to May 2020 before dramatically increasing in Q3 of 2020 and Q1 of 2021 with the initiation of COVID-19 vaccine studies. The average time to study startup increased for trials not related to COVID-19 and decreased for COVID-19-related trials. There has been no major impact on the number of open protocols throughout the course of the pandemic. CONCLUSION: Despite initial decreases in workload following the start of the COVID-19 pandemic, IDS operations returned to and, in some cases, exceeded prepandemic levels.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".