Fine-Needle Sspiration Biopsy versus Core-Needle Biopsy in Diagnosing Lung Cancer: A Systematic Review
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Bibliographic record
Abstract
BACKGROUND: Lung cancer leads cancer-related mortality in the world. The objective of the present systematic review was to compare fine-needle aspiration biopsy (fnab) with core-needle biopsy (cnb) for diagnostic characteristics and yields for diagnosing lung cancer in patients with lung lesions. METHODS: The medline and embase databases (from January 1, 1990, to September 14, 2009), the Cochrane Library (to Issue 4, 2009), and selected guideline Web sites were searched for relevant articles. RESULTS: For overall diagnostic characteristics (benign vs. malignant) of fnab and cnb, the ranges of sensitivity were 81.3%-90.8% and 85.7-97.4% respectively; of specificity, 75.4%-100.0% and 88.6%-100.0%; and of accuracy, 79.7%-91.8% and 89.0%-96.9%. For specific diagnostic characteristics of fnab and cnb (identifying the histologic subtype of malignancies or the specific benign diagnoses), the ranges of sensitivity were 56.3%-86.5% and 56.5-88.7% respectively; of specificity, 6.7%-57.1% and 52.4%-100.0%; and of accuracy, 40.4%-81.2% and 66.7%-93.2%. Compared with fnab, cnb did not result in a higher complication rate (pneumothorax or hemoptysis). No study has yet compared the diagnostic yields of fnab and of cnb for molecular predictive-marker studies in patients with lung lesions. DISCUSSION AND CONCLUSIONS: The evidence is currently insufficient to support a difference between fnab and cnb in identifying lung malignancies in patients with lung lesions. Compared with fnab, cnb might have a higher specificity to diagnose specific benign lesions. Well-designed, good-quality studies comparing fnab with cnb for diagnostic characteristics and yields in diagnosing lung cancer should be encouraged.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it