Preventing Measles in Immunosuppressed Cancer and Hematopoietic Cell Transplantation Patients: A Position Statement by the American Society for Transplantation and Cellular Therapy
Bibliographic record
Abstract
Until recently, measles exposures were relatively rare and so, consequently, were an afterthought for cancer patients and/or blood and marrow transplant recipients and their providers. Declines in measles herd immunity have reached critical levels in many communities throughout the United States due to increasing vaccine hesitancy, so that community-based outbreaks have occurred. The reemergence of measles as a clinical disease has raised serious concerns among immunocompromised patients and those who work within the cancer and hematopoietic cell transplantation (HCT) community. Since live attenuated vaccines, such as measles, mumps, and rubella (MMR), are contraindicated in immunocompromised patients, and with no approved antiviral therapies for measles, community exposures in these patients can lead to life-threatening infection. The lack of data regarding measles prevention in this population poses a number of clinical dilemmas. Herein specialists in Infectious Diseases and HCT/cellular therapy endorsed by the American Society of Transplant and Cellular Therapy address frequently asked questions about measles in these high-risk cancer patients and HCT recipients and provide expert opinions based on the limited available data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".