Bibliographic record
Abstract
Thrombocytopenia is the result of falling the number of platelet from 150,000/mi croL . There are three main reasons of thrombocytopenia, a-Decreasing of making platelet b-Increasing of destruction platelet c-Changing of distribution platelet. Pseudothrombocytopenia must be kept in mind too. Both hereditary and acquired reasons help thrombocytopenia have wide spreaded, but acquired causes are more common with increasing age. Thrombocytopenia separates three stages as numerical. Mild: 100,000 - 150,000/mi croL , Moderate: 50,000 - 100,000/mi croL . Severe: < 50,000/mi croL . However, thrombocytopenia is not usually detected clinically until the platelet count has fallen to levels below 100,000/mi croL . Severe thrombocytopenia, such as intracerebral and intra-abdominal bleeding may be life threatening . So diagnosing the treatment immediately can save the life. Transfusion of platelet may not need in all thrombocytopenias. Treatment of the underlying disease may be sufficient . The reason of thrombocytopenia can be temporary but also can be caused severe diseases. Causes of thrombocytopenia change development levels of countries, according to geographical distribution and application centers. In this review we emphasize common etiologies seen in adult patients with thrombocytopenia. doi:10.4021/jh28w
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".