Screening for Fabry Disease in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
Background: Fine-needle aspiration cytology (FNAC) is used as a screening test to evaluate lymphadenopathy. The combined use of genetic analysis and flow cytometry for immunophenotyping has increased the accuracy of diagnosis and correct categorisation of lymphomas on cytological preparations. Aim: To show the utility of immunocytochemistry and polymerase chain reaction (PCR) in the evaluation of cytological preparations of lymph nodes. Methods: Fine needle aspirates were obtained from 33 patients (initial presentation, n = 27; recurrence, n = 6). Routine examination was undertaken using immunocytochemistry and DNA PCR to detect clonality and specific translocations. The cytodiagnosis and subclassification of lymphoma was correlated with histological diagnosis in the available follow-up biopsies. Results: 14 patients had a cytological diagnosis of non-Hodgkin’s lymphoma (NHL), 4 had suspected NHL, 2 had atypical lymphoid proliferation and 13 had reactive hyperplasia. A World Health Organization (WHO) subtype was suggested in 8 patients. Incorporating the results of immunoglobulin heavy chain (IgH) and T-cell receptor (TCR) gene rearrangements enabled diagnosis of lymphoma in 17 patients, including 5 of the 6 patients suspected to have NHL or an atypical lymphoid proliferation. Identification of the translocations t (14;18) and t (2;5) helped WHO categorisation in 3 of the patients. The cytological findings were confirmed in 12 out of the 13 patients for whom histological follow-up was available. Seven of the 18 lymphoma patients were managed without a subsequent biopsy. We made one false–positive diagnosis of B-cell NHL on cytology. Conclusion: The use of immunocytochemistry and PCR is valuable in the definitive diagnosis and subtyping of malignant lymphomas on cytological preparations. The use of these techniques may avoid lymph node biopsies in some cases and allow definitive treatment based on aspirate findings alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".