Bibliographic record
Abstract
Abstract Context. —Histopathology, like other branches of medicine in West Africa, has suffered largely from economic, political, social, and infrastructural problems, becoming a shadow of the top quality that had been obtained in the past. To address the prevailing problems, one needs to attempt defining them. Objective. —The existing structure of training and practice are discussed, highlighting the author's perception of the problems and suggesting practical ways to address these while identifying potential roles for North American pathology organizations. Design. —The author's past and ongoing association with pathology practice in Nigeria forms the basis for this review. Results. —Pathology practice is largely restricted to academic medical centers. The largest of academic centers each accession around 4000 or fewer surgical specimens per year to train 9 to 12 residents. Histopathology largely uses hematoxylin-eosin routine stains, sometimes with histochemistry but rarely immunohistochemistry. Pathologists depend largely on their skills in morphology (with its limitations) to classify and subclassify tumors on routine stains, including soft tissue and hematolymphoid malignancies. Immunofluorescence, intraoperative frozen section diagnosis, electronic laboratory system, and gross and microscopic imaging facilities are generally not available for clinical use. Conclusion. —The existing facilities and infrastructure can be augmented with provision of material and professional assistance from other pathology associations in more developed countries and should, among other things, focus on supplementing residency education. Virtual residency programs, short-visit observerships, development of simple but practical laboratory information systems, and closer ties with pathologists in these countries are some of the suggested steps in achieving this goal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".