Assessing lumbar paraspinal muscle cross-sectional area and fat composition with T1 versus T2-weighted magnetic resonance imaging: Reliability and concurrent validity
Bibliographic record
Abstract
PURPOSE: Studies using magnetic resonance imaging to assess lumbar multifidus cross-sectional area frequently utilize T1 or T2-weighted sequences, but seldom provide the rationale for their sequence choice. However, technical considerations between their acquisition protocols could impact on the ability to assess lumbar multifidus anatomy or its fat/muscle distinction. Our objectives were to examine the concurrent validity of lumbar multifidus morphology measures of T2 compared to T1-weighted sequences, and to assess the reliability of repeated lumbar multifidus measures. METHODS: The lumbar multifidus total cross-sectional area of 45 patients was measured bilaterally at L4 and L5, with histogram analysis determining the muscle/fat threshold values per muscle. Images were later re-randomized and re-assessed for intra-rater reliability. Matched images were visually rated for consistency of outlining between both image sequences. Bland-Altman bias, limits of agreement, and plots were calculated for differences in total cross-sectional area and percentage fat between and within sequences, and intra-rater reliability analysed. RESULTS: T1-weighted total cross-sectional area measures were systematically larger than T2 (0.2 cm2), with limits of agreement <±10% at both spinal levels. For percentage fat, no systematic bias occurred, but limits of agreement approached ±15%. Visually, muscle outlining was consistent between sequences, with substantial mismatches occurring in <5% of cases. Intra-rater reliability was excellent (ICC: 0.981-0.998); with bias and limits of agreement less than 1% and ±5%, respectively. CONCLUSION: Total cross-sectional area measures and outlining of muscle boundaries were consistent between sequences, and intra-rater reliability for total cross-sectional area and percentage fat was high indicating that either MRI sequence could be used interchangeably for this purpose. However, further studies comparing the accuracy of various methods for distinguishing fat from muscle are recommended.
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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".