Lymphoma diagnosis at an academic centre: rate of revision and impact on patient care
Bibliographic record
Abstract
Few studies have examined the value of a mandatory second review of outside pathology material for haematological malignancies. Therefore, we compared diagnoses on biopsies referred to an academic medical centre to determine the rate and therapeutic impact of revised diagnoses resulting from a second review. We reviewed 1010 cases referred for lymphoma during 2009-2010. For each case, referral diagnosis and second review diagnosis were compared. Revised diagnoses were grouped into major and minor discrepancies and all major discrepancies were reviewed by a haematologist to determine the effect the diagnostic change would have on therapy. There was no change in diagnosis in 861 (85·2%) cases. In 149 (14·8%) cases, second review resulted in major diagnostic change, of which 131 (12·9%) would have resulted in a therapeutic change. The highest rates of revision were for follicular, high-grade B-cell, and T-cell lymphomas. We found higher rates of major discrepancy in diagnoses from non-academic centres (15·8%) compared to academic centres (8·5%; P = 0·022), and in excisional biopsies (17·9%) compared to smaller biopsies (9·6%; P = 0·0003). Mandatory review of outside pathology material prior to treatment of patients for lymphoma will identify a significant number of misclassified cases with a major change in therapy.
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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 | 0.012 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".