Distribution of ABO blood group in surgical patients at a tertiary hospital in rivers state, Nigeria
Bibliographic record
Abstract
The significance of determining the distribution of blood group of patients prior to surgical procedures cannot be over-emphasized. This is because surgical patients may need blood transfusion pre-operatively, intra-operatively or post-operatively. The distribution of ABO blood group varies in different regions of the world. Aim: To determine the distribution of ABO blood group in surgical patients at the Rivers State University Teaching Hospital (RSUTH). Method: This was a one-year retrospective study of Surgical patients (Surgery and Obstetrics/Gynaecology departments) of the RSUTH. The patients comprised of all the consecutive cases of the surgeries in these departments for the period under review. Ethical clearance was obtained from ethical committee of the Rivers State Hospital Management Board. Structured profoma was used to extract information from patients’ case notes and analyzed using SPSS version 25. Result: A total of 370 patients were attended to pre-operatively. There were 146 (39.5%) males and 224 (60.5%) females. The mean age was 31 years. The age range was 22 years to 56 years. One hundred and ninety four (52.4%) were obstetrics and gynaecological surgeries while 176 (47.6 %) were non-gynaecological surgeries. The commonest indication for surgery was caesarean representing 126 (34.1%) of the subject. The distribution of blood was as follows O 233 (63.0%), A 66 (17.8%), B 56 (15.1%), AB 15 (4.1%). Sex distribution of blood group O comprised of 140 (37.9%) females had blood O while 93 (25.1%) were males. Conclusion: Our study revealed the most prevalent blood group in surgical patients as blood group as O (63.0%) and the least prevalent blood group was blood group AB. The prime reason for ascertaining blood group especially in surgical patients is for transfusion of compatible blood when the need arises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".