RT education and COVID-19 pneumonia discharge quality
Bibliographic record
Abstract
Background There is a lack of data assessing the influence of respiratory therapist (RT) education on clinical outcomes. The primary objective of this study was to evaluate the impact of RTs holding advanced degrees or completing adult critical care competencies on discharge outcomes of patients with COVID-19 pneumonia. Study Design and Methods This retrospective, cross-sectional study included adults with confirmed COVID-19 admitted to the hospital for at least three days between March-May 2020. The academic degree held by each RT was considered advanced (baccalaureate or higher) or associate degree. Discharge outcomes were considered good, compromised, or poor when subjects’ hospital discharge was directly to home, long-term care facility/rehabilitation center, or hospice/died, respectively. A time-to-event multi-state regression model was used to determine the impact of RT academic degree and adult critical care competencies on discharge outcomes using α=0.05. Results A total of 260 subjects (median age 59 y; 166 males) received clinical care from 132 RTs. RT median professional experience was six y (IQR 3-11), 70.8% had an advanced degree, and 70.8% completed adult critical care competencies. The time-to-event multi-state regression model showed that patients with >85% exposure to RTs with advanced degrees transitioned 3.72 times more frequently to good outcomes than RTs without advanced degrees ( p =.001). Similarly, patients with >85% exposure to RTs with adult critical care competencies transitioned 5.10 times more frequently to good outcomes than RTs without adult critical care competencies ( p <.001). Conclusion Patients with COVID-19 pneumonia who received greater than 85% of their care by RTs who earned advanced degrees or completed adult critical care competencies had improved discharge outcomes. This preliminary work suggests that advancing education for the respiratory therapist workforce may improve the discharge quality of patients with acute respiratory failure and should be further explored.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".