Using the Laser in Correcting Anemia in Hemodialysis Patients
Bibliographic record
Abstract
One of the main symptoms of terminal‐stage chronic renal insufficiency is anemia. One of the best applicable methods correcting anemia is using recombinant human erythropoietin preparation. Using recombinant human erythropoietin in patients with terminal‐stage chronic renal insufficiency in 90–95% of events had a positive effect, but 5–10% of patient had refraction to erythropoietin, which has spurred the search for new efficient methods correcting anemia. The purpose of the study was to determine the influence of the laser on erythropoiesis and blood acid–alkaline condition (pH) in patients with terminal‐stage chronic renal insufficiency. In the course of the study, erythrocytes, hemoglobin, reticulocytes in blood, and blood acid–alkaline condition (pH) were determined. At the beginning of the treatment, all hematological parameters 5 and 15 days after marrow stimulation were defined. 15 days after marrow stimulation with laser, increasing amounts of erythrocytes, hemoglobin, and hematocrit were observed. The initial erythrocyte count was 2.22 ± 0.1 × 1012/L, hemoglobin 67.7 ± 3.2 g/L and hematocrit 18.2 ± 1.2%. During the laser treatment, erythrocyte count increased up to 2.9 ± 0.8 × 1012/L, hemoglobin up to 89.6 ± 2.9 g/L and hematocrit up to 28.2 ± 1.3% (p < 0005).
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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".