The correlation between severity scores in computed tomography lung scans and viral load in the severity of novel coronavirus 2019 progression
Bibliographic record
Abstract
BACKGROUND: This study aimed to find the correlation between severe computed tomography (CT) lung scores and nasopharyngeal viral load (Ct value) in the severity of COVID-19 disease progression. METHOD: In this study, 37 patients diagnosed with COVID-19 were categorized into severely ill and not severely ill samples. Their Ct values, epidemiological data, lung CT, and laboratory test results were collected three times, respectively, on the first day of their hospital admission, 3-5 days thereafter, and prior to hospital discharge. Among the 37 patients, 8 progressed from not severely ill to severely ill; we also paid attention and observed changes in clinical parameters of COVID-19 patients who entered our city from other cities (imported cases) and the infected local residents who contacted these imported patients (non-imported cases). RESULTS: Among the 37 patients, the Ct values and lung severity scores (LSSs) were similar in imported and non-imported cases (F = 0.59 and 2.56; p = 0.45 and 0.12, respectively) but the proportion of severely ill imported patients was significantly higher compared with non-imported patients (F = 7.77; p = 0.01). Additionally, 21.6% of patients' illness worsened; lymphocyte counts and Ct values were significantly lowered, and C-reactive protein and LSS significantly increased during COVID-19 disease progression. Furthermore, LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values (Pearson's rank = -0.763, -0.824, and -0.588; p = 0.028, 0.012, and 0.003, respectively). CONCLUSION: In the severity of COVID-19 disease progression, nasopharyngeal viral load and lung CT severity were closely related, and LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".