Comparative diagnostic efficacy of serum squamous cell carcinoma antigen in hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is a common liver malignancy in Nigeria. Hepatitis B and C viruses, alcohol and Aflatoxin B are among the various aetiologies. More work needs to be done in the search for markers that will aid early detection of this condition as it is uniformly fatal once advanced. Alphafetoprotein (AFP) remains the most widely used tumour marker of HCC detection in spite of its known shortcomings. The objective of this study was to determine the efficacy of serum squamous cell carcinoma antigen (SCCA) , in comparison to alphafetoprotein in the detection of HCC. METHOD: Sixty patients with HCC and thirty apparently healthy controls attending the Medical Outpatient Department(MOPD) of the University College Hospital Ibadan(UCH) Nigeria were selected for the study. Questionnaire was used to collect clinical data while AFP, SCCA levels, serum HBsAg and anti-HCV were determined using ELISA method- (Diagnostic Automation Inc. Canada). Abdominal ultrasound scan was also done. RESULT: Thirty one (51.7%) out of 60 selected cases were positive for HBsAg while six (20%) out of 30 controls were positive for HBsAg(p = 0.004). Out of the 60 cases selected for this study, only 2 (3.3.%) cases were positive for hepatitis C virus, while only 1(3.3%) out of 30 control was positive for hepatitis C virus(p = 0.74).The mean AFP value for cases with HCC was 393.21 ng/ml ±386.97 compared to the control group which was 5.60 ± 13.03 ng/ml (p value 0.001). The mean SCCA level was 0.64 ± 0.56 ng/ml and 0.71 ± 0.65 ng/ml for cases and controls respectively (p = 0.631). CONCLUSION: Alphafetoprotein remains a good tumour marker for the diagnosis of HCC. Serum squamous cell carcinoma antigen(SCCA) has no discriminatory power and may not be useful as a tumour marker for Nigerians with hepatocellular carcinoma.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".