Platelet Count to Spleen Diameter Ratio for the Diagnosis of Esophageal Varices: Is It Feasible?
Bibliographic record
Abstract
AIM: To study the value of platelet count to spleen diameter ratio as a noninvasive parameter for diagnosing esophageal varices (EVs) in liver cirrhosis. METHODS: The laboratory and ultrasonographic variables were prospectively evaluated in 150 patients with liver cirrhosis. Only stable patients were included in the study. Patients with active gastrointestinal bleeding at the time of admission were excluded. All patients underwent screening upper gastrointestinal endoscopy. RESULTS: The platelet count, spleen diameter and platelet count to spleen diameter ratio in patients with EVs were significantly different from patients without EVs. The platelet count to spleen diameter ratio had the highest accuracy among the three parameters. By applying receiver operating characteristic curves, a platelet count to spleen diameter ratio cut-off value of 1014 was obtained, which gave positive and negative predictive values of 95.4% and 95.1%, respectively. The accuracy of this cut-off value as evaluated by applying receiver operating characteristic curves was 0.942 (95% CI 0.890 to 0.995). CONCLUSION: Among the noninvasive parameters studied, platelet count to spleen diameter ratio had the highest accuracy for diagnosing EVs. However, the evidence for the noninvasive diagnosis is not yet sufficient to replace endoscopy as a diagnostic screening tool for EVs in all cirrhotic patients. The platelet count to spleen diameter ratio may be a useful tool for diagnosing EVs in liver cirrhosis noninvasively when endoscopy facilities are not available.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".