Severe lung injury and lung biopsy in children post‐hematopoietic stem cell transplantation: <scp>T</scp>he differences between allogeneic and autologous transplantation
Bibliographic record
Abstract
To review outcome of children post-allogeneic (allo) and autologous (auto) SCT with severe lung injury who had lung biopsy and to determine whether the diagnoses provided by lung biopsy had an impact on outcome. Retrospective study was carried out from January 2000 to June 2010. Nine hundred and eighteen children (0-18 yr) received SCT (allo 476, auto 442), and 59 biopsies were performed in 48 patients. Most common result of lung biopsy was non-infectious inflammation and recurrent disease in allo- and autorecipients, respectively. In a multivariate analysis, survival of allorecipients who had management change was inferior (p = 0.002; HR: 3.12). These patients were extremely sick, and management change was the last attempt to stabilize their respiratory status. There was a trend toward superior survival for children who had biopsy after 100 days following SCT (p = 0.09; HR: 0.55) and a trend toward inferior survival for those with proven infections within two wk of biopsy (p = 0.07; HR: 2.14). Only 31% of allorecipients and 25% of autorecipients survived. There were no biopsy-related complications. Lung biopsy itself appears to be well tolerated, although requiring a biopsy seems to carry a poor prognosis; this seems to be due to different causes, auto (relapse), allo (non-infectious inflammation).
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".