Effect Of Anemia On Cognitive Capacity Of Adult Students In Sokoto, Nigeria
Bibliographic record
Abstract
Anemia due to iron deficiency or inherited sickle cells nowadays threatens public health in many respects. Among the effects of anemia is its ability to affect cognitive or related abilities. Thus, this study aimed to assess the effect of iron deficiency anemia and sickle cell anemia on the cognitive ability of participants in Sokoto. The study design involved recruiting 50 participants (25 healthy and 25 tested to be anemic) who were subjected to Montreal Cognitive Assessment. Another fifteen sickle anemia patients, and twenty-five healthy persons were evaluated using Montreal cognitive assessment. The scores of all the respondents were recorded and subjected to the X2 test and revealed significant differences at (p<0.05). The result of the study indicated that the anemic participants scored fewer mean marks (420.0 ± 14.0) in contrast to the healthy participants (820.0 ± 32.6) at (P<0.05). The effect of sickle cell anemia was revealed with a significant difference (p<0.05), showing that the healthy adult participants of the study scored higher marks (240.0 ± 16.0) compared to the anemic participants (924.0 ± 30.8). Thus, the anemia of any kind can potentially affect the cognitive capacity of students in the state.
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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 | 0.001 | 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) | 0.002 | 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".