Survival after transfusion as assessed in a large multistate US cohort
Bibliographic record
Abstract
BACKGROUND: The only survival and mortality data on a general population of transfused patients in the United States is more than two decades old. More contemporary data are needed to reflect more current patient populations and transfusion practices. STUDY DESIGN AND METHODS: Data were extracted from Constella Health Strategies Sciences' managed-care administrative claims database that contains private health care claims. Patients were selected if they had at least one professional or facility claim indicating transfusion in 1995. Only the first transfusion in the time period was included so that each patient was counted only once and all claims were unduplicated. Survival for five years after transfusion was the primary outcome measure. RESULTS: A total of 6779 patients were included in the analysis. A total of 4658 (69%) patients were alive 1 year after transfusion, 4056 (60%) were alive at 2 years, and 3092 (46%) were alive 5 years after transfusion. Overall annual mortality was 31 percent in Year 1 after transfusion, 14 percent in Year 2, and 10 percent in each of Years 3 through 5. Transfusion mortality was much higher in recipients older than age 65 at the time of transfusion, who comprised 60 percent of transfused patients. CONCLUSION: These data from the mid 1990s can be used in models of the effectiveness of risk reduction interventions and in models of the disease consequences of infections transmitted through blood transfusions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".