The clinical features, management and prognosis of primary and secondary indolent lymphoma of the bone: a retrospective study of the International Extranodal Lymphoma Study Group (IELSG #14 study)
Bibliographic record
Abstract
Indolent lymphomas primarily involving the skeleton (iPBL) represent < 1% of all primary bone lymphomas. The management and prognosis have not been previously described. Patients with primary and secondary iPBL were selected from an international database of 499 patients with a histopathological diagnosis of non-Hodgkin lymphoma and skeleton involvement, and clinical features, management and prognosis were analyzed. Twenty-six (5%) patients had an iPBL. Ten patients had small lymphocytic lymphoma, 10 had follicular lymphoma and six had lymphoplasmacytic lymphoma. Eleven patients had limited stage and 15 had advanced disease. The overall response rate was 73% (95% confidence interval [CI] = 57-89%). Median follow-up was 58 months, and the 5- and 10-year progression-free survival (PFS) rates were 37 ± 10% and 25 ± 12%, respectively. Nine patients are alive, with 5- and 10-year overall survival (OS) rates of 46 ± 10% and 29 ± 11%, respectively. Patients with small lymphocytic lymphoma showed significantly better outcome than patients with follicular lymphoma. Performance status and stage of disease were independently associated with OS. The prognosis of patients with primary bone lymphoplasmacytic or follicular lymphoma was less favorable.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".