Does CT help in reducing RT-PCR false negative rate for COVID-19?
Bibliographic record
Abstract
Abstract Background: Early detection is the key to contain the ongoing pandemic. The current gold standard to detect SARS CoV2 is RT-PCR. However, it has a high false negative rate and long turnaround time. Purpose: In view of the high sensitivity of CT in detection of lower respiratory tract pathologies, a study of 2581 patients comparing RT-PCR status with CT findings was undertaken to see if it augments the diagnostic performance. Materials and Methods: A multi centre prospective study of consecutive cases was conducted. All CT studies suggestive of COVID 19 pneumonia were collated and evaluated independently by three Radiologists to confirm the imaging diagnosis of COVID-19 pneumonia. The RT-PCR values were retrospectively obtained, based on the RT-PCR values, CT studies were categorised into three subgroups, positive, negative and unknown. CT features from all three groups were compared to evaluate any communality or discordance. Results: Out of the 2581 patients with positive CT findings for COVID pneumonia, 825 were females and 1,756 were males in a wide age group of 28-90 years. Predominant CT features observed in all the subgroups were Ground glass densities 94.8%, in mixed distribution (peripheral and central) (59.12%), posterior segments in 92% and multilobar involvement in 70.9%. The CT features across the three subgroups were statistically significant with a P value <0.001. Conclusion: There was a communality of CT findings regardless of RT-PCR status. In a pandemic setting ground glass densities in a subpleural, posterior and basal distribution are indicative of COVID 19. Thus CT chest in conjunction to RT PCR augments the diagnosis of COVID 19 pneumonia; utilization of CT chest may just be the missing link in closing this pandemic.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".