Current and Emerging Roles of Whole-Body MRI in Evaluation of Pediatric Cancer Patients
Bibliographic record
Abstract
Their paper is entitled "Current and Emerging Roles of Whole-Body MRI in the Evaluation of Pediatric Cancer Patients." Doctors, welcome to our podcast. So Ravi I'm going to begin with you. Your paper reviews the use of whole-body MRI as and imaging tool for children with known cancer and for those who are being screened for cancer predisposition syndromes. The article begins with the review of the technical aspects of performing these studies and in particular the utility of coronal STIR sequences in these examinations. Let's show Table First off I want to say Dr. Klein thanks so much for having all of us. We're all very excited to talk about this topic that we think is of importance to thethe pediatric radiology community. So for whole-body MR examinations I think the big principle is that we're referring in a most strict sense to imaging from vertex to toes, but that can be modified to be sort of multiple contiguous body regions like the neck, chest, abdomen and pelvis; limiting to that depending on the indications. I think the big overarching theme I want to start with is just by saying that you know I think it's important to get diagnostically useful information within a reasonable timeframe with this examination because you can really have sequences that end up taking a lot longer if you're not a little bit judicious with how you select them. So that's where these sequences come in. So I think it comes down to having two key principles which is the right hardware and also good sequence selection that are sort of high yield for the diagnostic purpose you want. So I'll quickly just talk about the hardware so I think one principle for this with respect to hardware is to try and minimize time that's spent basically shifting coils around and moving patients around in between the different sequences and so to the extent that you have hardware like a moving table platform for example and coils that you can position at the beginning of the examination that will allow you to seamlessly transition from one body station to the next, that can be very helpful in minimizing the lag time in between sequences. So that's a big thing. And I think having dedicated multi-channel coils as well that are surface coils as opposed to using the inbore magna coil can be very helpful in improving your signal to noise ratio and thereby minimizing time and maximizing quality. So that's
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".