Forensic Considerations for Preprocessing Effects on Clinical <scp>MDCT</scp> Scans
Bibliographic record
Abstract
Manipulation of digital photographs destined for medico-legal inquiry must be thoroughly documented and presented with explanation of any manipulations. Unlike digital photography, computed tomography (CT) data must pass through an additional step before viewing. Reconstruction of raw data involves reconstruction algorithms to preprocess the raw information into display data. Preprocessing of raw data, although it occurs at the source, alters the images and must be accounted for in the same way as postprocessing. Repeated CT scans of a gunshot wound phantom were made using the Toshiba Aquilion 64-slice multidetector CT scanner. The appearance of fragments, high-density inclusion artifacts, and soft tissue were assessed. Preprocessing with different algorithms results in substantial differences in image output. It is important to appreciate that preprocessing affects the image, that it does so differently in the presence of high-density inclusions, and that preprocessing algorithms and scanning parameters may be used to overcome the resulting artifacts.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".