Elevated distress among patients undergoing screening for lung cancer
Bibliographic record
Abstract
Objective: Low-dose CT scanning has recently been recommended to screen patients at elevated risk of developing lung cancer, however, limited data exist describing distress experienced by this at-risk population. The objective of this study is to describe the prevalence and risk factors of high distress among patients undergoing screening for lung cancer. Methods: The validated National Comprehensive Cancer Network Distress Thermometer (DT) was used to evaluate distress prior to and following lung cancer screening among 228 patients attending the Center for Lung Cancer Screening and Prevention at the Stony Brook Cancer Center between September 30, 2013 and September 29, 2014. Clinically significant distress was defined by a score ≥ 4 on the DT instrument and logistic regression models were used to evaluate factors associated with high distress. Results: Forty-three percent of study participants experienced elevated distress prior to screening, while approximately one-third of patients reported distress scores ≥ 4 post-screening. Risk factors for elevated distress before screening included female gender (OR = 2.68; 95% CI [1.51, 4.77]) and having a positive family history of lung cancer (OR = 2.02 [1.04, 3.91]), while significant associations with post-screening distress were found among females (OR = 3.16 [1.73, 5.80]), current smokers (1.85 [1.00, 3.42]) and those with a positive personal history of a non-cancerous lung diagnosis (OR = 1.87 [1.00, 3.51]). Conclusions: The lung cancer screening population is a vulnerable group burdened by increased levels of distress. The screening visit represents a unique opportunity to not only educate patients about lung health and smoking cessation but additionally to address issues related to psychological wellness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".