Diagnostic sensitivity of pleural fluid cytology in malignant pleural effusions: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pleural fluid cytology is an important diagnostic test used for the investigation of pleural effusions. There is considerable variability in the reported sensitivity for the diagnosis of malignant pleural effusions (MPE) in the literature. OBJECTIVE: The purpose of this review is to determine the diagnostic sensitivity of pleural fluid cytology for MPE, both overall and by tumour type, to better inform the decision-making process when investigating pleural effusions. DATA SOURCES: A literature search of EMBASE and MEDLINE was performed by four reviewers. Articles satisfying inclusion criteria were evaluated for bias using the QUADAS-2 tool. DATA EXTRACTION: For quantitative analysis, we performed a metaanalysis using a binary random-effects model to determine pooled sensitivity. Subgroup analysis was performed based on primary cancer site and meta-regression by year of publication. SYNTHESIS: 95.5%). For primary thoracic malignancies, sensitivity was highest in lung adenocarcinoma (83.6%; 95% CI 77.7% to 89.6%) and lowest in lung squamous cell carcinoma (24.2%; 95% CI 17.0% to 31.5%) and mesothelioma (28.9%; 95% CI 16.2% to 41.5%). For malignancies with extrathoracic origin, sensitivity was high for ovarian cancer (85.2%; 95% CI 74.2% to 96.1%) and modest for breast cancer (65.3%; 95% CI 49.8% to 80.8%). CONCLUSIONS: Pleural fluid cytology has an overall sensitivity of 58.2% for the diagnosis of MPE. Clinicians should be aware of the high variability in diagnostic sensitivity by primary tumour type as well as the potential reasons for false-negative cytology results.PROSPERO registration numberCRD42021231473.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".