Analysing COVID-19 treatment outcomes in dedicated wards at a large university hospital in northern Poland: a result-based observational study
Bibliographic record
Abstract
OBJECTIVES: Presenting outcomes of patients hospitalised for COVID-19 should be put in context and comparison with other facilities. However, varied methodology applied in published studies can impede or even hinder a reliable comparison. The aim of this study is to share our experience in pandemic management and highlight previously under-reported factors affecting mortality. We present outcomes of COVID-19 treatment in our facility that will allow for an intercentre comparison. We use simple statistical parameters-case fatality ratio (CFR) and length of stay (LOS). SETTING: Large clinical hospital in northern Poland serving over 120 000 patients annually. PARTICIPANTS: Data were collected from patients hospitalised in COVID-19 general and intensive care unit (ICU) isolation wards from November 2020 to June 2021. The sample consisted of 640 patients-250 (39.1 %) were women and 390 (60.9 %) were men, with a median age of 69 (IQR 59-78) years. RESULTS: Values of LOS and CFR were calculated and analysed. Overall CFR for the analysed period was 24.8%, varying from 15.9 % during second quarter 2021 to 34.1% during fourth quarter 2020. The CFR was 23.2% in the general ward and 70.7% in the ICU. All ICU patients required intubation and mechanical ventilation, and 44 (75.9 %) of them developed acute respiratory distress syndrome. The average LOS was 12.6 (±7.5) days. CONCLUSIONS: We highlighted the importance of some of the under-reported factors affecting CFR, LOS and thus, mortality. For further multicentre analysis, we recommend broad analysis of factors affecting mortality in COVID-19 using simple and transparent statistical and clinical parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".