Eligibility for Nirmatrelvir/Ritonavir Among Adults With an Acute Care <scp>COVID</scp> ‐19 Encounter: A Retrospective Cohort Study From Alberta, Canada
Bibliographic record
Abstract
PURPOSE: To describe the characteristics of adults who had a severe coronavirus disease 2019 (COVID-19) outcome during a timeframe that nirmatrelvir/ritonavir (NMV-r; Paxlovid) was available, to assess medical eligibility and receipt of NMV-r before presentation to acute care. METHODS: Population-level administrative data was used to describe adults (≥ 18 years) who had an acute care COVID-19 encounter (hospitalization or emergency department visit with COVID-19) between January 2022 and March 2023 in Alberta, Canada. RESULTS: The 30 793 adults had an acute care COVID-19 encounter; among the 8345 (27.1%) who were assessed as medically eligible for NMV-r, 8111 (97.2%) did not receive outpatient NMV-r ≤ 30 days before the encounter. During the acute care COVID-19 encounter, the proportion who had an all-cause death was 4.7% (received prior NMV-r treatment) and 6.6% (did not) among those who were medically eligible for NMV-r. Of those medically eligible for NMV-r who were hospitalized (n = 4495), the mean length of stay was 9.7 days (SD 11.1) in NMV-r treated and 15.3 days (SD 25.9) in untreated, and the proportion admitted to the intensive care unit was 2.1% in NMV-r treated and 7.6% in untreated. CONCLUSION: Results from this real-world study show that most adults with an acute care COVID-19 encounter who were assessed as medically eligible for NMV-r at the time of presentation had not received this outpatient treatment beforehand in Alberta, Canada, suggesting a potential care gap. If gaps are closed, strain on the acute healthcare system can be alleviated, with attendant improvement in other important patient outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".