New Cancers after Autotransplantations for Multiple Myeloma
Bibliographic record
Abstract
We describe baseline incidence and risk factors for new cancers in 4161 persons receiving autotransplants for multiple myeloma in the United States from 1990 to 2010. Observed incidence of invasive new cancers was compared with expected incidence relative to the US population. The cohort represented 13,387 person-years at-risk. In total, 163 new cancers were observed, for a crude incidence rate of 1.2 new cancers per 100 person-years and cumulative incidences of 2.6% (95% confidence interval [CI], 2.09 to 3.17), 4.2% (95% CI, 3.49 to 5.00), and 6.1% (95% CI, 5.08 to 7.24) at 3, 5, and 7 years, respectively. The incidence of new cancers in the autotransplantation cohort was similar to age-, race-, and gender-adjusted comparison subjects with an observed/expected (O/E) ratio of 1.00 (99% CI, .81 to 1.22). However, acute myeloid leukemia and melanoma were observed at higher than expected rates with O/E ratios of 5.19 (99% CI, 1.67 to 12.04; P = .0004), and 3.58 (99% CI, 1.82 to 6.29; P < .0001), respectively. Obesity, older age, and male gender were associated with increased risks of new cancers in multivariate analyses. This large data set provides a baseline for comparison and defines the histologic type specific risk for new cancers in patients with MM receiving postautotransplantation therapies, such as maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".