Cytopathology Including Fine-Needle Aspiration in Sub-Saharan Africa: A Cameroon Experience
Bibliographic record
Abstract
Abstract Context .—Surgical pathology is unavailable in most of sub-Saharan Africa because of equipment costs and lack of expertise. Cytopathology is an inexpensive and reliable alternative. Objective .—To explore the utility of cytopathology in a rural hospital setting in Africa. Design .—A cytopathologist and a pathology resident from Calgary, Alberta, Canada, went to Cameroon to provide a cytopathology service at the Banso Baptist Hospital. Both performed the fine-needle aspiration procedures. Direct smears were fixed in alcohol and stained with hematoxylin-eosin. Surgical specimens subsequently obtained from the patients were processed and reported at Calgary Laboratory Services, Canada. The histopathologic diagnoses were the gold standard for determining the accuracy of the cytologic diagnoses. Results .—Fifty-nine patients were examined during a 5-week period, 33 females (56%) and 26 males (44%). Sixteen (27%) were known to be HIV positive. Forty-four fine-needle aspiration procedures were performed for 43 patients (73%). The cost of each procedure was approximately US $10. Head and neck and breast were the sites most frequently sampled for aspirates. Cervical smears from 5 patients were also assessed, as were 8 fluid specimens and 2 touch preparations of prostatic core biopsies. The most frequent diagnoses for malignancy were carcinoma and lymphoma. Tuberculous lymphadenitis was diagnosed in 6 patients, 4 of whom were HIV positive. Surgical specimens were received from 18 patients (30%). Cytohistologic and clinicopathologic correlation revealed 1 false-positive (1.6%) and 1 false-negative (1.6%) diagnosis. Conclusion .—Cytopathology is a reliable alternative for tissue diagnosis in low-resource settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".