Patterns of Pretreatment Diagnostic Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Non-Small Cell Lung Cancer (NSCLC): Special Characteristics in the COVID Pandemic and Influence on Outcomes
Bibliographic record
Abstract
The pandemic raised a discussion about the postponement of medical interventions for non-small cell lung cancer (NSCLC). We analyzed the characteristics of pretreatment diagnostic assessment in the pandemic and the influence of diagnostic assessment on outcomes. A total of 96 patients with stereotactic body radiation therapy (SBRT) for NSCLC were included. The number of patients increased from mean 0.9 (2012–2019) to 1.45 per month in the COVID era (p < 0.05). Pandemic-related factors (contact reduction, limited intensive care unit resources) might have influenced clinical decision making towards SBRT. The time from pretreatment assessment (multidisciplinary tumor board decision, bronchoscopy, planning CT) to SBRT was longer during the COVID period (p < 0.05). Reduced services, staff shortage, or appointment management to mitigate infection risks might explain this finding. Overall survival, progression-free survival, locoregional progression-free survival, and distant progression-free survival were superior in patients who received a PET/CT scan prior to SBRT (p < 0.05). This supports that SBRT guidelines advocate the acquisition of a PET/CT scan. A longer time from PET/CT scan/conventional staging to SBRT (<10 vs. ≥10 weeks) was associated with worse locoregional control (p < 0.05). The postponement of diagnostic or therapeutic measures in the pandemic should be discussed cautiously. Patient- and tumor-related features should be evaluated in detail.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".