Simplified Criteria Using Pleural Fluid Cholesterol and Lactate Dehydrogenase to Distinguish between Exudative and Transudative Pleural Effusions
Bibliographic record
Abstract
BACKGROUND: An important part of the investigation of pleural effusion is the identification of markers that help separate exudate from transudate. OBJECTIVES: The purposes of this study were to compare the accuracy of published and new sets of criteria to distinguish between exudative and transudative pleural effusions, and to determine whether serum biochemical analysis is necessary. METHODS: An externally validated cohort study was performed. Pleural effusions were determined to be transudative or exudative on the basis of an assessment of the medical record by two clinicians blinded to biochemical results. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the receiver operating characteristic curve were determined for each proposed combination of criteria. RESULTS: Pleural fluid analysis was available for 311 thoracenteses in the main cohort and for 112 thoracenteses in the validation cohort. The best sensitivity (97% [95% CI 94-99]) and negative likelihood ratio (0.04 [95% CI 0.02-0.08]) for identifying exudative effusions were observed with criteria combining pleural fluid lactate dehydrogenase greater than 0.6 the upper limit of normal serum lactate dehydrogenase and pleural fluid cholesterol greater than 1.04 mmol/L (40 mg/dL). The overall diagnostic accuracy was similar to Light's criteria. Findings were similar in the validation cohort. CONCLUSIONS: Our proposed criteria using simultaneously pleural fluid lactate dehydrogenase and pleural fluid cholesterol can identify an exudate with a sensitivity and an overall diagnostic accuracy similar to Light's criteria. It avoids simultaneous blood sampling, thus reducing patient discomfort and potential costs.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".