Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it