Liver iron overload assessment by <i>T</i> magnetic resonance imaging in pediatric patients: An accuracy and reproducibility study
Bibliographic record
Abstract
date of transplant, conditioning regimen, engraftment data (neutrophils and platelets), response, and survival data were derived from review of medical records.Determination of candidacy for ASCT and conditioning regimen were at the discretion of the treating physician.Baseline Charlson comorbidity risk index (CCI) and hematopoietic stem cell transplant comorbidity index (HCT-CI) were calculated for all patients.Comorbidity data were collected from review of medical records, and comorbidities were scored as previously published [6,7].For both indices, a score of 0 was defined as low risk, a score of 1-2 was defined as intermediate risk, and a score of 3 or higher was considered high risk.Comorbidity risk scores were correlated with NRM, OS, and PFS.Primary endpoints for the analysis were progression-free survival (PFS) and overall survival (OS).OS was defined as the time from transplant until death or last follow up, and PFS was defined as the time from transplant until disease progression, death or last follow up.Treatment-related mortality (TRM) was defined as death within 100 days of transplant not attributed to progression of lymphoma.Analyses of OS and PFS were performed using the Kaplan-Meier method [12].Confidence intervals were computed using a 95% confidence level.Confidence intervals for the median survival times were computed using Brookmeyer and Crowley method [13].Analysis was carried out using R-A language and environment for statistical computing [14].
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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 | 0.012 | 0.029 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".