Accuracy of magnetic resonance imaging for measuring maturing cartilage: A phantom study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the accuracy of magnetic resonance imaging measurements of cartilage tissue-mimicking phantoms and to determine a combination of magnetic resonance imaging parameters to optimize accuracy while minimizing scan time. METHOD: Edge dimensions from 4 rectangular agar phantoms ranging from 10.5 to 14.5 mm in length and 1.25 to 5.5 mm in width were independently measured by two readers using a steel ruler. Coronal T1 spin echo (T1 SE), fast spoiled gradient-recalled echo (FSPGR) and multiplanar gradient-recalled echo (GRE MPGR) sequences were used to obtain phantom images on a 1.5-T scanner. RESULTS: Inter- and intra-reader reliability were high for both direct measurements and for magnetic resonance imaging measurements of phantoms. Statistically significant differences were noted between the mean direct measurements and the mean magnetic resonance imaging measurements for phantom 1 when using a GRE MPGR sequence (512x512 pixels, 1.5-mm slice thickness, 5:49 min scan time), while borderline differences were noted for T1 SE sequences with the following parameters: 320x320 pixels, 1.5-mm slice thickness, 6:11 min scan time; 320x320 pixels, 4-mm slice thickness, 6:11 min scan time; and 512x512 pixels, 1.5-mm slice thickness, 9:48 min scan time. Borderline differences were also noted when using a FSPGR sequence with 512x512 pixels, a 1.5-mm slice thickness and a 3:36 min scan time. CONCLUSIONS: FSPGR sequences, regardless of the magnetic resonance imaging parameter combination used, provided accurate measurements. The GRE MPGR sequence using 512x512 pixels, a 1.5-mm slice thickness and a 5:49 min scan time and, to a lesser degree, all tested T1 SE sequences produced suboptimal accuracy when measuring the widest phantom.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it