Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline
Bibliographic record
Abstract
CLINICAL QUESTION: The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty. CURRENT PRACTICE: There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing. RECOMMENDATIONS: In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk. HOW THIS GUIDELINE WAS CREATED: This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure. THE EVIDENCE: A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed. UNDERSTANDING THE RECOMMENDATION: The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".