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.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".