The abuse of ultrasonography: A critical review of the Nigerian situation
Bibliographic record
Abstract
Ultrasonography, a widely used diagnostic tool, has experienced significant proliferation in Nigeria's healthcare landscape. However, alongside its increased availability comes a concerning trend of abuse, raising questions about ethical practice, patient welfare, and regulatory oversight. This abstract critically reviews the abuse of ultrasonography in Nigeria, examining key factors contributing to this phenomenon and its implications for healthcare delivery. Firstly, the proliferation of unqualified practitioners highlights a fundamental issue, with individuals lacking proper training and credentials performing ultrasound scans, leading to inaccurate diagnoses and patient harm. Secondly, the misuse of ultrasonography for gender determination purposes has led to sex-selective abortions, raising ethical concerns and perpetuating gender-based discrimination. Furthermore, diagnostic inaccuracies and misinterpretations due to inadequate training and expertise among practitioners pose risks to patient safety. The absence of a robust regulatory framework exacerbates these challenges, allowing unscrupulous practitioners to operate with impunity. Ethical considerations surrounding informed consent, patient confidentiality, and respect for patient autonomy are compromised in cases of ultrasound abuse. Addressing the abuse of ultrasonography in Nigeria requires coordinated efforts to strengthen regulatory mechanisms, enhance professional standards, and promote ethical practices. By acknowledging the root causes of abuse and fostering accountability in medical practice, Nigeria can ensure the safe and responsible use of ultrasonography for improved healthcare outcomes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".