[Correlation of the Desialylation of Platelets with Efficacy of the First-line Therapy for ITP].
Bibliographic record
Abstract
OBJECTIVE: To detect desialylation of platelets in primary immune thrombocytopenia(ITP) patients with FITC-labelled ECL and RCA-1, and compare the correlation of the desialylation level and the efficacy of first-line therapy for ITP. METHODS: Before treatment, 48 ITP patients were selected and their levels of ECL and RCA-1 were detected with flow cytometry. RESULTS: The desialylation level in the different efficacy groups by using the first-line therapy of corticosteroids and (or) intravenous immunoglobulin G (IVIG) had a statistically significant difference (P<0.05). The correlation analysis showed negative relation of the therapeutic efficacy with desialylation level, that is to say, the more high of desialylation level, the more poor therapeutic efficacy of the first-line therapy. CONCLUSION: The desialylation level of platelets in ITP patients is related with the first-line therapeutic efficacy, the efficacy for patients with high desialylation level is poor, suggesting that the FcR-independent pathway exists in clearance of platelets in ITP patients. Therefore, the desialylation level of platelets may suggest the first-line therapeutic efficacy for ITP patients to a certain degree, and may be used as a potential target for the treatment of refractory ITP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".