Design and application of a self‐evaluation questionnaire for individuals at a high‐risk of lung cancer
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to establish a comprehensive evaluation system to assess the risk factors of lung cancer for the general population. METHODS: With the method of evidence-based medicine, risk factors of lung cancer were identified and their risk assignments were calculated to design the Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer. Studies including more than 10 000 subjects were carried the out to confirm the questionnaire's value. RESULTS: The questionnaire consisted of 15 risk factors and their risk assignments, such as sex, age, smoking, female passive smoking, previous illness histories, exposure to harmful gases, mental depression and genetic susceptibility. In the population application, data from 30 lung cancer patients revealed its desired reliability and validity. The next pre-investigation, including 94 patients and 252 controls, confirmed its differentiating power, and encouraged a much larger-scale survey with 2161 subjects to determine the threshold (T) to identify high-risk individuals, the threshold was 116 points. According to this criterion, 1537 high-risk volunteers and 6556 controls were recruited to participate in a 3-year follow-up study from 2007 to 2009. There were 31 cases of lung cancer detected in the high-risk group, with a detection rate of 2.02%, significantly higher than that of the controls (5/6556, 0.08%), indicating an excellent predictive value of the questionnaire. CONCLUSIONS: The Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer was a good means for evaluating the risks of lung cancer.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".