Evaluation of Lymphadenopathy and Suspected Lymphoma in a Lymphoma Rapid Diagnosis Clinic
Bibliographic record
Abstract
PURPOSE: Lymphomas often present a diagnostic challenge, and for some a delay in diagnosis can negatively influence outcomes of therapy. We established a nurse practitioner-led lymphoma rapid diagnosis clinic (LRDC) with the goal of reducing wait times to definitive diagnosis. We examined the initial 30-month experience of the LRDC, and results were compared with time periods before implementation of the clinic to determine program impact. METHODS: All patients referred to LRDC with suspicion of lymphoma from June 1, 2015 to Nov 30, 2017 were evaluated. Time from initial consultation to diagnosis was compared with patients diagnosed at our center with lymphoma in 2008 and 2012. Patient symptoms and relevant laboratory/imaging findings were collected to identify patterns of presentation and predictive factors for benign diagnoses. RESULTS: < .001). By univariable analysis, lymph node size greater than 3.4 cm and presence of mediastinal or abdominal adenopathy increased the likelihood of a diagnosis of malignancy, whereas younger age, being a nonsmoker, and prior rheumatologic condition were associated with a nonmalignant diagnosis. In multivariable analysis, lymph node size, age, and prior rheumatologic diagnosis remained significant. CONCLUSION: Establishing a nurse practitioner-led LRDC was effective in shortening time to diagnosis of lymphoma. Younger age, smaller lymph node size, and prior rheumatologic disorder reduced the likelihood of a cancer diagnosis in our patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".