Role of fine needle aspiration biopsy cytology in the diagnosis of infections
Bibliographic record
Abstract
The role of fine needle aspiration biopsy (FNAB) cytology in diagnosing infections has expanded due to the increase in the number of immune compromised patients and the increasing role of FNAB in the developing world where infection is a major cause of illness. FNAB has become the first procedural test in cases where the clinical and imaging findings suggest an infectious lesion or where there is a differential diagnosis of infection or metastatic or primary tumor. This applies to FNAB of palpable or image directed or deep seated lesions accessed by EUS and EBUS. This article details a recommended approach and technique for FNAB of infectious lesions, and discusses the role of rapid on site evaluation and the application of ancillary testing including the rapidly expanding array of molecular tests based on FNAB material. The utility of recognizing suppurative and granulomatous infectious patterns in FNAB direct smears, and the specific cytomorphological features on routine Papanicolaou and Giemsa stains and on special stains of FNAB smears is described for a large number of bacterial, fungal, viral, parasitic, and protozoan infections. The role of cytopathologists is to now train cytopathologists in sufficient numbers to provide FNAB services, teach trainee cytopathologists and cytotechnologists, and to encourage our clinical colleagues to use FNAB in the diagnosis of infections and other lesions to the benefit of patients and the medical system. Diagn. Cytopathol. 2016;44:1024-1038. © 2016 Wiley Periodicals, Inc.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".