COVID-19 and lung cancer
Bibliographic record
Abstract
COVID-19 pandemic had affected health services around the world, also reducing the diagnosis of lung cancer. On the other hand, examination of surgical specimens in patients with lung cancer and SARS-CoV-2 gave the opportunity to evidence early histologic features related to this emerging pandemic. Different prioritization of health organizations during COVID-19 pandemic resulted in a significant decline of lung cancer screening (up to 56%), delayed diagnosis (up to 30-40%) and higher advanced stage, with some exceptions (i.e., Canada). Increased use of stereotactic radiation treatments in stage I-IIA have been noticed in better-organized health systems. Surgical specimens performed for lung cancer in patients with incipient SARS-CoV-2 permitted to appreciate early histologic findings of COVID-19 with hyperplastic pneumocytes with/without fibrin exudate, alveolar macrophages/myeloid cells, perivascular T-lymphocytic infiltrate and lack of hyaline membrane. While the COVID-19 pandemic has declined the rate of lung cancer diagnosis worldwide, some institutions have significantly limited detrimental effects. Histology related to early SARS-CoV-2 infection in surgical samples for lung cancer revealed specific histologic changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".