Intensity-Based Computation Tomography (CT) Evaluation of Temporal Changes in Lung Abnormalities During the Recovery Stage in COVID-19 In-Hospital Patients
Bibliographic record
Abstract
Abstract Background: The coronavirus disease-19 (COVID-19) and its variants have increased rapidly worldwide since December 2019, with respiratory disease being a prominent complication. As such, optimizing evaluation methods and identifying factors predictive of disease progress remain critical. The purpose of the study was to assess late phase (≥3 weeks) pulmonary changes using intensity-based computed tomography (CT) scoring in COVID-19 patients and determine the clinical characteristics predicting lung abnormalities and recovery.Methods: We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. Two scoring methods were assessed: (1) area-based scoring (ABS) and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by the calculating the averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results: Using the ABS system, temporal changes in lung abnormalities during recovery were highly variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). Strong relationships were observed with D-dimer and C-reactive protein (CRP) levels on admission, with hazard ratios (HR)(95%CI) of 5.32 (1.25-22.6)(P=0.026) and 1.05 (1.10-1.09)(P=0.017), respectively. Conclusion: Our results suggest COVID-19-mediated pulmonary abnormalities persist well-beyond 3-weeks of symptom onset, with intensity-weighted rather than area-based scoring being more sensitive. Moreover, D-dimer and CRP levels were predictive of the recovery from the disease.
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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 | 0.001 |
| 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".