The relationship between chronic anemia caused by hematologic disease and cognitive impairment
Bibliographic record
Abstract
Abstract Patients with hematology disease ( such as aplastic anemia, primary myelofibrosis, and myelodysplastic syndrome) always in the condition of moderate and severe anemia for a long time. However, this chronic anemia condition impact on cognitive function was not well studied. We aim to explore the relationship between chronic anemia and cognitive function. We conducted a cross-sectional study. Collecting patients’ clinical dates and demographic characteristics from blood routine examination and self report. Objective cognition function was assessed by Chinese versions of Montreal Cognitive Assessment ( MoCA), total score of cognitive function and subscores of cognitive domains were calculated for each. Associations with chronic anemia and cognitive function were estimated using logistic regression. A total of 214 people including 70 chronic anemia and 144 non-anemia. Chronic anemia was independent factor for overall cognitive impairment, visual space and execution, attention, abstract and delayed recall (P < 0.05). The longer time of chronic anemia, the more possibility to have cognitive decline (P < 0.05). 36.5 months is a cutoff line for cognitive impairment among patients with chronic anemia. Chronic anemia can cause cognitive impairment; the longer time of chronic anemia, the easier to have cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".