Comparing average breast fat content results from two different protocols at 1.5T and 3T: Can the data be pooled?
Bibliographic record
Abstract
PURPOSE: To compare the total breast fat content computed from two separate studies, performed on different scanners and with different protocols, with the goal of defining a relationship to allow pooling the data. MATERIALS AND METHODS: Twelve healthy volunteer women were scanned with two different protocols on the same day. The protocols differed in four important aspects: vendors (GE vs. Philips), scanner main magnetic field strengths (1.5T vs. 3T), pulse sequences (2D fast spin-echo vs. 3D spoiled gradient-echo), and water/fat separation techniques. The resulting water and fat maps were processed with in-house software to extract breast tissue slice-wise. Percent fat content was calculated for each breast, per subject. RESULTS: Total percent fat contents (averaged across both breasts) resulting from both protocols were plotted against each other, on a subject-by-subject basis, revealing a strong correlation (R(2) > 0.99), with an overestimation of the fat content from Protocol 1 relative to Protocol 2. The proposed T2 TE-correction for Protocol 1 improves the correlation while decreasing the discrepancy between protocols. CONCLUSION: Total breast fat content of healthy women resulting from the two protocols can be pooled using a linear relationship. The proposed T2 TE-corrected Protocol 1 is expected to yield accurate fat content.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".