Potential Predictors of Poor Prognosis among Critical COVID-19 Pneumonia Patients Requiring Tracheal Intubation
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a global public health concern that can be classified as mild, moderate, severe, or critical, based on disease severity. Since the identification of critical patients is crucial for developing effective management strategies, we evaluated clinical characteristics, laboratory data, treatment provided, and oxygenation to identify potential predictors of mortality among critical COVID-19 pneumonia patients. We retrospectively utilized data from seven critical patients who were admitted to our hospital during April 2020 and required mechanical ventilation. The primary endpoint was to clarify potential predictor of mortality. All patients were older than 70 years, five were men, six had hypertension, and three ultimately died. Compared with survivors, non-survivors tended to be never smokers (0 pack-years vs. 30 pack-years, p = 0.08), to have higher body mass index (31.3 kg/m2 vs. 25.3 kg/m2, p = 0.06), to require earlier tracheal intubation after symptom onset (2.7 days vs. 5.5 days, p = 0.07), and had fewer lymphocytes on admission (339 /μL vs. 518 /μL, p = 0.05). During the first week after tracheal intubation, non-survivors displayed lower values for minimum ratio of the partial pressure of oxygen to fractional inspiratory oxygen concentration (P/F ratio) (44 mmHg vs. 122 mmHg, p < 0.01) and poor response to intensive therapy compared with survivors. In summary, we show that obesity and lymphopenia could predict the severity of COVID-19 pneumonia and that the trend of lower P/F ratio during the first week of mechanical ventilation could provide useful prognostic information.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".