Subclassification of lymphoproliferative disorders in serous effusions
Bibliographic record
Abstract
BACKGROUND: Rare studies have reported the application of multiple ancillary tests to the diagnosis of lymphoproliferative disorder in serous effusions. In the current study, the authors evaluated the effectiveness of using an algorithm for the triage of serous effusions and the contribution of ancillary studies to achieve a specific subtype of lymphoproliferative disorder. METHODS: Serous effusion samples that had a final diagnosis of lymphoproliferative disorder or suspicious for lymphoma were selected from cases that were diagnosed between 2001 and 2010. Data were collected on patient and sample characteristics as well as results from immunophenotype and molecular studies. RESULTS: In total, 168 serous effusions were identified from 110 patients. The most common site of involvement was the pleural cavity (n = 133) followed by the peritoneal cavity (n = 30) and pericardial cavity (n = 5). The volume of serous effusions ranged from 2 mL to 1000 mL (mean, 238 mL). In 42 patients (38.2%), serous effusions were the primary source of diagnosis. In 129 patients who had a diagnosis of LPD, either generic (n = 82) or specific (n = 47) ancillary tests were performed as a single test in 58 samples (67.4%) or as a combination of multiple studies in 19 samples (23.2%). Immunophenotyping was successful in almost all samples that had a specific subtype with 16 B-cell and 4 T-cell lymphomas being diagnosed. More samples with a specific subtype of lymphoma underwent molecular tests compared with those who had a generic diagnosis (19.1% vs 13.4%). CONCLUSIONS: Successful, specific subtyping of lymphoproliferative disorders was achieved in approximately 33% of cases that were tested for ancillary studies following an approach for the triage and aliquoting of serous effusion samples.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".