Bibliographic record
Abstract
No studies exist documenting that chemotherapy alone eradicates tumors composed of leukemic cells in a large group of patients with tumors at any one site. Yet, its use has continued over 40 years in the absence of data. Consensus protocols exist only for testis and meningeal tumors, relying on local therapy. To constitute a body of knowledge about tumors at one site, the breast was chosen and all published cases were analyzed, with follow-up obtained, to document the behavior of acute leukemia tumors and survival after presentation. Among 235 cases (52% published since 2000), overall survival was poor, particularly for the 43% with concurrent morphologic marrow relapse, with 66-73% one-year mortality. Only 4 of 106 patients treated with chemotherapy alone survived 4 years. The majority of AML and ALL tumors were only transiently responsive to anti-leukemia treatments, including transplant, and next relapses were as, or more, common in further tumors than in marrow. A pattern of tumors similar to the metastases of invasive lobular breast cancer was revealed. When relapse occurred in marrow, durable remission was only rarely obtained. These data suggest a potential benefit of incorporating extent of disease workup at diagnosis and relapse into prospective trials. This could yield an accurate incidence of extramedullary tumors and a means to identify occult residual disease which could lead to marrow relapse. This approach could potentially result in greater success in curing acute leukemias.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".