Magnetic resonance imaging versus histological assessment for estimation of lesion volume after experimental spinal cord injury
Bibliographic record
Abstract
OBJECT: The purpose of this study was to compare measures of lesion volume obtained by means of 1.5-T MR imaging to those obtained by the Cavalieri method, 6 weeks after experimental spinal cord injury. METHODS: Nine male Wistar rats were subjected to spinal cord injury by clip compression (50 g) at the T-4 level. Six weeks postinjury, the rats were sacrificed, and spinal cords were analyzed ex vivo for lesion volume by means of 1.5-T MR imaging and subsequently, by the Cavalieri method. In the latter method, cords were cut longitudinally in 25-microm sections and stained with solochrome cyanin for myelin. The area of the lesion was determined for each serial section, and the distance-weighted sum of all area measures was then calculated to estimate the total lesion volume. RESULTS: Bland-Altman analysis showed that the 2 methods had an acceptable level of agreement for lesion volume estimation, but the Cavalieri method was prone to an overestimation bias. The MR imaging estimates of lesion volume were greater than the Cavalieri method estimates in 3 spinal cords, but the difference between measures was within 1 standard deviation of perfect agreement in these 3 lesions, and the mean difference between measures was 18.3%. In contrast, in those lesions in which the Cavalieri method yielded larger lesion volumes (5 lesions), the difference between measures was 2 standard deviations away from perfect agreement for 2 animals and the mean difference between measures was 72.4%. CONCLUSIONS: The results illustrate that the overestimation bias of the Cavalieri method is due, in part, to artifacts produced during processing of the spinal cord tissue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".