Reestablishing Diagnostic Anatomical Pathology Services by a Fine-Needle Aspiration Clinic in Monrovia, Liberia
Bibliographic record
Abstract
Two civil wars and the Ebola epidemic left Liberia with essentially no reliable anatomical pathology services. There had been absolutely no cytology, histology, or forensic services in Monrovia, the nation’s capital. The needs have been assessed and a long-term plan was made by consulting the local physicians and administrators. Potential workforce and financial resources have been identified along with the most suitable location to set up a fine-needle aspiration (FNA) clinic as the first step to rebuild the diagnostic services. The first FNA was performed on January 8, 2018, in the JFK Hospital in Monrovia, and it was followed by 64 other procedures until the end of February. Forty-nine female and 16 male patients went through the diagnostic procedures. Forty-four samples resulted in a benign and 14 in a malignant diagnosis, while 7 were nondiagnostic. Diff-Quick stain was used to prepare the slides, and due to the limitations, cell blocks were not performed for ancillary studies. The diagnostic pathology reports can assist the clinicians now to build a better treatment plan for the patients, and they will also help to build a national diagnostic database. Liberia’s health care is still in very poor shape, and the lack of reliable diagnostic services is one of the biggest challenges. FNA has turned out to be a cost-effective method to provide a diagnostic assessment. However, in the long run, consistent planning, persistent joint efforts by the local and foreigner organizations and physicians, and a lot of volunteer help will be needed to improve the pathology services and education in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".