Coagulation parameters in lung cancer patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Hypercoagulability in lung cancer patients is associated with a high incidence of mortality and morbidity in the world. Therefore, this meta-analysis aimed to explore the correlation of the basic coagulation abnormalities in lung cancer patients compared with the control. METHOD: PubMed, Scopus, and other sources were employed to identify eligible studies. The outcome variable was expressed using mean ± standard deviation (SD). Heterogeneity among studies and publication bias were evaluated. The quality of included studies was also assessed based on Newcastle-Ottawa Scale checklist. RESULT: Finally, through a total of eight studies, prolonged prothrombin time (PT; standard mean difference [SMD]: 1.29; 95% CI: 0.47-2.11), plasma D-dimer value (SMD 3.10; 95% CI 2.08-4.12), fibrinogen (SMD 2.18; 95% CI:1.30-3.06), and platelet (PLT) count (SMD 1.00; 95% CI 0.84-1.16) were significantly higher in lung cancer patients when compared with the control group. The single-arm meta-analysis also showed that compared with control, lung cancer patients had high pooled PT 13.7 (95% CI:12.2-15.58) versus 11.79 (95% CI = 10.56-13.02), high D-dimer 275.99 (95% CI:172.9-11735.9) versus 0.2 (95% CI:0.20-0.37), high plasma fibrinogen 5.50 (95% CI:4.21-6.79) versus 2.5 (95% CI:2.04-2.91), and high PLT count 342.3 (95% CI:236.1-448.5) versus 206.6 (95% CI:176.4-236.7). CONCLUSION: In conclusion, almost all the coagulation abnormalities were closely associated with lung cancer, and hence coagulation indexes provide an urgent clue for early diagnosis and timely management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| 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".