Iron deficiency anaemia in moderate to severely anaemic patients.
Bibliographic record
Abstract
BACKGROUND: Anaemia is defined as a clinical abnormality characterized by reduction in haemoglobin concentration below the normal for age, sex, physiological condition and altitude from the sea level of a person. It can be of different types, the commonest being iron deficiency anaemia which affects mostly pregnant and lactating females and growing children in the developing world. The present study was conducted at Ayub Teaching Hospital on this problem of global importance. METHODS: One hundred consecutive anaemic patients were selected from Ayub Teaching Hospital. After detailed history and examination, preliminary blood tests including full blood counts, platelets count, retics count, absolute blood values and blood film examination (all tests done by visual method) were done in the laboratories of Ayub Teaching Hospital and Ayub Medical College, Abbottabad. A clinical diagnosis was made based upon the findings of history, examination and blood tests. In patients suspected to have iron deficiency anaemia, serum iron studies (i.e. serum iron, Total iron binding capacity (TIBC) and serum ferritin) were done to confirm the diagnosis. RESULTS: Sixty eight patients were found to have iron deficiency anaemia. Females were affected more than the males. Female to male ratio being 1:5:1. Majority of the patients (82.2%) were aged 21-60 years. However, there were two peaks (21% & 25%) in age groups 21-30 and 41-50 respectively. Based upon serum ferritin concentrations, most of the patients were found to suffer from moderate iron deficiency anaemia (53%). CONCLUSION: Iron deficiency anaemia is the commonest type of anaemia in our area. It is more common in females. It is more common in age group 21-60 years, with two peaks, one in 21-30 years and the other in 41-50 years.
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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 | 0.001 |
| 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".