New Biochemical Parameters in the Differential Diagnosis of Ascitic Fluids
Bibliographic record
Abstract
Background: In the cases of ascitis, it is essential to determine their origin using the parameters obtained by the cytological and biochemical examinations. The aim of this study was to evaluate the usefulness of different biochemical markers and the number of cells in the differential diagnosis of ascitic fluid (AF). Methods: One hundred ninety-one cases of AF were studied, who were admitted to the hospital from January 01, 2009 to December 31, 2014. One hundred fifty-two of them were included in the analysis, and the remaining 39 were excluded because they had more than one associated pathology, clotted or hemolyzed. Results: The more frequent etiologies of AF were the cirrhosis (29%), the infections (22%) and the neoplasies (19%). Other pathologies reached 16%. Cutoff > 300 cells/mm 3 detected the 78% of exudates. The AF/serum (S) of aspartate aminotransferase (AST) (> 0.5), lactate dehydrogenase (LDH) (> 0.6), proteins (PT) (> 0.5), cholesterol (COL) (> 0.4), and alanine aminotransferase (ALT) (> 0.5) correctly detected 80%, 78%, 72%, 70% and 70% of the exudates, respectively. Conclusion: We proposed the utilization of a new cutoff of cellular counting, major of 300/mm 3 , since it would allow improving the detection of exudate ascites, without including the transudate ascites. AST AF/serum ratio (AF/S) showed the major usefulness in the differentiation and characterization of AF; LDH, proteins, cholesterol and ALT might be also acceptable in the above mentioned differentiation. The serum-ascites albumin gradient (SAAG) turned out to be a good marker of portal hypertension associated with cirrhotic processes. Creatine kinase (CK), alkaline phosphatase (ALP), amylase (AMI), total bilirubin (TB), triglycerides (TG) and glucose (GLU) did not allow differentiating exudates from transudates. Gastroenterol Res. 2016;9(1):17-21 doi: http://dx.doi.org/10.14740/gr700w
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".