Has the covid pandemic influenced the diagnosis of patients with lung cancer?
Bibliographic record
Abstract
Introduction: On March 14, 2020, a general confinement was decreed in Spain due to the development of the COVID19 pandemic, affecting health care worldwide. Objective: to measure the management of study times and the characteristics of patients with lung cancer studied in the Pneumology service of a Spanish tertiary hospital, before, during and after confinement during the COVID19 pandemic. Material and methods Retrospective, descriptive and cross-sectional study of patients with a histological diagnosis of lung cancer studied in a tertiary hospital from 01.01.2019 to 03.31.2022. The patients studied during period 1, defined as prior to confinement (01.01.2019-14.03.2020), period 2, from confinement until the end of 2020, and period 3, from the beginning of 2021 to the first quarter of 2022 have been analysed. The number of patients, their clinical characteristics and cancer staging, as well as the differences in symptom times until referral, duration of the diagnostic study until the decision of the tumour committee and time until the start of the first treatment have been analysed. SPSS 22 program has been used for the statistical analysis. Results: 584 patients have been studied, 228 in period 1, 84 in period 2 and 272 in period 3. Table 1: general variables. Table 2: differences between periods. Conclusions: 1- The initial months of the pandemic caused a decrease in the number of diagnosed lung cancers. 2- Despite this, we have not observed significant differences in the stage or in the situation of the patient at diagnosis in the different periods of time analysed 3- No significant differences been observed in the times until consultation, diagnosis or 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".