Do Plasma Cells Affect Hemoglobin and Red Blood Cell Counts in Patients With and Without Myeloma?
Bibliographic record
Abstract
Background: We undertook this study to evaluate if there is any significant correlation between hemoglobin levels, red blood cell (RBC) count, bone marrow normoblast percentage and marrow plasma cell percentage in patients with and without myeloma. Methods: In this study, we compared hemoglobin level, red blood cell count and bone marrow normoblast percentage with plasma cell percent in 60 patients with and without myeloma (30 with and 30 without myeloma). Results: There was significant negative correlation for all three in the myeloma group (P values of 0.03, 0.02 and < 0.0001 for hemoglobin, red cell count and normoblast percent respectively). In the non myeloma group significant negative correlation (P 0.02) was seen only between normoblast and plasma cell percent. The students-t-test between the two groups revealed a significant difference between mean red cell count and normoblast percentage, however the mean hemoglobin was similar. We further sub classified the myeloma group based on hemoglobin level and found that the commonest subgroup had average hemoglobin of 84 g/L. Conclusions: We conclude that in the non myeloma group there is possibility of plasma cells affecting bone marrow normoblast count. We have also raised the possibility of absence of ineffective erythropoiesis in the myeloma group. doi: http://dx.doi.org/10.4021/jh38w
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".