Incidence of pulmonary embolism and its risk factors in lung cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Pulmonary embolism (PE) not only threatens quality of life (QOL) but also poses a direct threat to life itself. This study was aimed at evaluating the incidence of PE and its risk factors in LC patients by meta-analysis (MA). METHODS: A search was done in electronic databases such as PubMed, EMBASE, Web of Science, and the Cochrane Library. Quality assessment was performed using the Newcastle Ottawa Scale (NOS). MA was conducted using Stata16.0. RESULTS: A total of 18 studies were included with a sample size of 216,344 in total. The incidence of PE in LC patients was 8.3% (95% CI 6.9%-9.6%). Risk factors for PE in LC patients included adenocarcinoma as a pathologic type (OR = 2.61, 95% CI 1.87-5.11), the advanced stage as a tumor stage (OR = 2.15, 95% CI 1.90-3.63), chemotherapy (OR = 2.42, 95% CI 1.86-3.72), concomitant DVT (OR = 4.42,95% CI 2.56-11.68), hemoglobin ≥ 140 g/L (OR = 2.25, 95% CI 2.10-4.60), D-dimer ≥ 500 mmol/L (OR = 4.27, 95% CI 1.76-24.34), and elevated CEA (OR = 2.69, 95% CI 1.85-3.93). CONCLUSION: PE is a common complication in LC patients. Close follow-up and a comprehensive screening program are important, especially for high-risk patients who receive chemotherapy or have advanced cancer, adenocarcinoma, concomitant DVT, or high levels of hemoglobin/D-dimer/CEA.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".