Optimizing density of intrathecal contrast at digital subtraction myelography: Evaluation of differing needle characteristics and injection rates in a phantom
Bibliographic record
Abstract
Dynamic myelography, performed as digital subtraction myelography or dynamic computed tomography myelography, is crucial in diagnosing intracranial hypotension resulting from a cerebrospinal fluid-venous fistula (CVF). The quality of the myelogram is paramount for accurate diagnosis. Using a phantom, the impact of needle type (Quincke vs. Whitacre), caliber, side-hole position, and rate of injection on the quality of the myelogram was determined. The ideal decubitus myelogram would provide a large volume of a hyperdense contrast within the lateral dependent aspect of the thecal sac, optimally flooding the mouths of the neural foramina and root sleeves where the vast majority of CVFs originate. The results of this study suggest it is exclusively the rate of injection that most predictably dictates the quality of the myelogram in this regard. Specifically, a slow injection rate, on the order of 0.1 mL/s, should be opted for to decrease turbulence, optimize myelogram quality, and thus improve CVF detection in clinical practice.
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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 | 0.000 |
| 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 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".