Computation Tomography (CT)-Based Evaluation of Temporal Changes in Lung Abnormalities During the Recovery Stage in COVID-19 In-Hospital Patients
Bibliographic record
Abstract
Abstract Objectives: To assess the late phase CT changes of COVID-19 patients, and figure out factors predicting lung abnormality in late phase. Methods: We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19 admitted between February 7, 2020 and March 27, 2020. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. CT images were analyzed by 2 independent radiologists using different scoring: (1) area-based scoring (ABS); and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results : Temporal changes in lung abnormalities during recovery (weeks 3 through 8) of CT findings using the ABS system were variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during the late phase of recovery in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). In assessing the correlation between averaged AUCs and clinical characteristics, 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 an intensity-weighted rather than area-based scoring system is more sensitive to detect subtle temporal CT changes in COVID-19, with D-dimer and CRP levels on admission being predictive of the time course of late phase recovery from the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".