Prolonged SARS-CoV-2-RNA Detection from Nasopharyngeal Swabs in an Oncologic Patient: What Impact on Cancer Treatment?
Bibliographic record
Abstract
The pandemic of SARS-CoV-2 is a serious global challenge affecting millions of people worldwide. Cancer patients are at risk for infection exposure and serious complications. A prompt diagnosis of SARS-CoV-2 infection is crucial for the timely adoption of isolation measures and the appropriate management of cancer treatments. In lung cancer patients the symptoms of infection 19 may resemble those exhibited by the underlying oncologic condition, possibly leading to diagnostic overlap and delays. Moreover, cancer patients might display a prolonged positivity of nasopharyngeal RT-PCR assays for SARS-CoV-2, causing long interruptions or delay of cancer treatments. However, the association between the positivity of RT-PCR assays and the patient's infectivity remains uncertain. We describe the case of a patient with non-small cell lung cancer, and a severe ab extrinseco compression of the trachea, whose palliative radiotherapy was delayed because of the prolonged positivity of nasopharyngeal swabs for SARS-CoV-2. The patient did not show clinical symptoms suggestive of active infection, but the persistent positivity of RT-PCR assays imposed the continuation of isolation measures and the delay of radiotherapy for over two months. Finally, the negative result of SARS-CoV-2 viral culture allowed us to verify the absence of viral activity and to rule out the infectivity of the patient, who could finally continue her cancer treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".