A székletkalprotektin meghatározásának szerepe a bélbetegségek diagnosztikájában és kezelésében
Bibliographic record
Abstract
The diagnostics of gastrointestinal diseases have evolved significantly in the past few decades. Besides endoscopy and conventional imaging modalities, there is a growing interest for rapid point-of-care laboratory tests to help discriminate between diseases with similar clinical symptoms and/or help the follow-up of chronic conditions, predicting relapses. The fecal calprotectin testing is a routine diagnostic tool in many countries. It is also more and more accessible in Hungary as well. We aim to present a short review on the role and performance of fecal calprotectin test in the diagnosis and follow-up of gastrointestinal diseases, especially inflammatory bowel diseases, gastrointestinal infections, irritable bowel syndrome and pediatric conditions. By presenting the different cut-off values, sensitivity and specificity rates representative for each disease, we hope to further aid clinicians in decision-making regarding these conditions. Orv Hetil. 2019; 160(9): 322-328.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".