Assessment of bootstrap resampling accuracy for PET data
Bibliographic record
Abstract
Bootstrap resampling has been successfully used in estimating statistical properties of PET images by generating a set of statistically equivalent datasets based on one or more original datasets. However, the bootstrap resampling is only valid when the original dataset well represents the underlying distribution. The purpose of this work is to assess the validity of nonparametric bootstrap resampling using a long acquisition of a planar brain phantom, ensuring a good representation of the underlying distribution of all possible events. The assessment is carried out in two stages corresponding to the two `worlds': i) the real world-generation of K reference list-mode datasets with five statistical levels (0.01%, 0.1%, 1%, 10% and 20% of the original dataset) using resampling with replacement of the statistically very rich original dataset playing the role of the population; and ii) the bootstrap world-generation of equivalent K bootstrap replicates using five resampled dataset from stage i) for each of the five statistical levels. The distributions from the two stages or worlds are then compared using the metric of Jensen-Shannon (J-S) divergence to quantify the similarity of the two distributions from stages i) and ii). In order to apply the J-S divergence two different histogramming methods are used: i) with constant and ii) adaptable binning. The bootstrap distributions are found to be constantly different to the real world distributions regardless of the data size and binning method. However when statistics are very low (for single voxels and 0.01% datasets) the comparison fails as the distributions are limited by the non-negativity constraint.
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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 | 0.056 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".