Pleural fluid protein is inversely correlated with age in uncomplicated parapneumonic pleural effusions
Bibliographic record
Abstract
Objectives: Assess whether age influences standard biochemical parameters used in the differential diagnosis of transudative and exudative pleural effusions. Design and methods: We retrospectively analyzed data from the database of our clinic from 225 patients with pleural effusions categorized based on their final diagnosis in 5 groups: transudates 41 (18%), uncomplicated parapneumonic 26 (12%), complicated parapneumonic 20 (9%), tuberculosis 35 (15%) and lung cancer 103 (46%). We tested whether age correlated with pleural fluid protein or lactate dehydrogenase. Results: There was a statistically significant inverse correlation only between the age and the pleural fluid protein content in patients with uncomplicated parapneumonic effusions with correlation coefficient r= -0.6 [(95% CI= -0.8 to -0.28); p = 0.001]. Linear regression analysis showed that this association is given by the equation: age = 101.998-10.03 protein. In the same group of patients age was not correlated with serum protein content. Conclusions: Our study shows that age maybe a confounding factor in the differential diagnosis of transudative and exudative pleural effusions. Clinicians should be aware of this finding especially when dealing with elders. (c) 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".