Impact of the COVID-19 pandemic on drug-related issues and pharmacist interventions in geriatric acute care units
Bibliographic record
Abstract
To assess and compare the activity of pharmaceutical analysis on drug management in a geriatric acute care unit prior to and during the COVID-19 pandemic. This was a single-centre, observational, retrospective, and comparative cohort study. All Pharmacist Interventions (PIs) carried out in the unit between 27 January 2020 and 30 April 2020 were distinguished according to whether they were conducted prior to or during the first wave of COVID-19. The main outcome measure was the rate of PIs per patient and per line of treatment analysed. Other data collected were the drug class managed by the PI, the Drug Related Problems (DRP) identified, the nature of the advice given, and the acceptance rate by geriatricians A total of 355 stays were analysed, with PIs generated for 21.7% of the stays prior to COVID-19, and for 53.4% of the stays during the first wave (p=1.029 E-9). Among the 4,402 lines of treatments analysed, 54 PIs were carried out for prescriptions prior to COVID-19, and 177 during the first wave (p=0.002). DRPs were mostly related to anti-infectious drugs during the pandemic (20.3%, p=0.038), and laxatives prior to the pandemic (13.0%, p=0.023). The clinical impact of the PIs was mainly moderate (43.7%). The acceptance rate was 59.3%. A greater amount of DRPs were detected and more therapeutic advice was proposed during the first wave of COVID-19, with a focus on drugs used for the management of COVID-19 rather than geriatric routine treatments. The needs for clinical pharmacists were strengthened during the pandemic.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".