Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers
Bibliographic record
Abstract
Dynamic myraidpro contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".