Letter to the Editor regarding “Opportunistic screening with CT: comparison of phantomless BMD calibration methods”
Bibliographic record
Abstract
The paper by Bartenschlager and colleagues1 addresses an important topic in the field of osteoporosis by exploring methods to perform density calibration on CT data so that the BMD can be calculated in the absence of a calibration phantom. They focused on the vertebra to examine 4 methods of internal calibration and commented on their accuracy for applications to opportunistic CT. One of the methods assessed was developed in our lab,2 which they refer to as the “voxel-specific” method, and it was concluded that it provides poor results at the spine. While they have done an important service by comparing the available methods, we are writing to highlight some key points to consider, which are related to the voxel-specific approach as the field rapidly progresses. First, it is important to note, as correctly stated by Bartenschlager and colleagues, that the voxel-specific method is the only considered approach that does not require direct measurements on the scanner used to acquire the image. This is a significant advantage because it can be applied in settings where access to the CT scanner is unavailable and therefore supports opportunistic screening broadly in a variety of conditions. It is not specific to any individual CT device or the acquisition parameters used (eg, X-ray energy and table height), which provides the benefit of universality. The other methods require pre-calibration or reference datasets, which are appropriate in a controlled setting (eg, clinical trial), but they are limited for generalized opportunistic CT use.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.036 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".