Correction of an anemia in patients with a terminal stage chronic renal insufficiency on haemodialysis
Bibliographic record
Abstract
One of the basic symptoms of a terminal stage chronic renal insufficiency is anemia. From everything, used methods of correction of an anemia, it is considered the most effective application of preparations recombinant human erythropoietin (r‐Hu EPO). Since 1994 in the Scientific Centre of Surgery begins application r‐Hu EPO. Application r‐Hu EPO in patients with a terminal stage chronic renal insufficiency in 90–95% of cases had a positive effect, but 5–10% of patients have intolerance to erythropoietin, that has induced to search of new effective methods of correction of anemia. During research were determined quantity erythrocytes, hemoglobin, reticulocyte in peripheral blood and acid‐alkaline condition of blood. All hematology parameters were defined at the beginning of treatment, over 5 day and for 15 day of stimulation of a bone marrow. For 15 days after stimulation of a bone marrow by the laser there was an authentic increase of quantity erythrocyte, hemoglobin, hematocrit. The initial contents erythrocytes made 2.22 ± 0.1 10 × 12, hemoglobin 67.7 ± 3.2 g/l and hematocrit 18.2 ± 1.2%. During treatment by the laser parameters erythrocytes have increased up to 2.9 ± 0.8 10 × 12, hemoglobin up to 89.6 ± 2.9 g/l and hematocrit up to 28.2 ± 1.3%(P < 0,005). Hematology parameters in blood of control group authentically have not changed.
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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) | 0.001 | 0.000 |
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".