Forced oscillation technique used for monitoring of covid-19 pneumonia
Bibliographic record
Abstract
Background: Forcrd oscillation technique (FOT) is a non-invasive method for investigation of lung mechanics without active participation of the patient. The objectives of the study were to find out whether FOT method could be used for monitoring of covid-19 pneumonia (CVP) course and how FOT indices correlate with other, commonly used indicators of disease severity. Methods: During the hospital stay and 3 months after the discharge from hospital repeated measurements of lung mechanics were performed with portable device Tremoflo-100 (Thorasys, Canada). Results: Most relevant differences between disease stages reflected the lung reactance indices - Fres and AX. Indices characterizing the airflow resistance didn’t reach the significance level. Correlation analysis also was performed between FOT indices and CT score, body mass index (BMI), patients age, blood CRP and ferritin levels, duration of hospital stay and patients oxygen demand (FiO2). Significant correlations were found only between the last two. The highest significance showed Fres % pred and AX % pred. Fres correlated to FiO2 with R=0,498 and p-0,0000004, but AX% with r=0,502 Conclusions: The study has shown that FOT method reflects the changes in lung mechanics occurring during acute phase and recovery period from covid-19 pneumonia. FOT indices correlate with patients oxygen demand and hospital stay-time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".