Spontaneous intracranial hypotension due to CSF–venous fistula: Evaluation of renal accumulation of contrast following decubitus myelography and maintained decubitus CT to improve fistula localization
Bibliographic record
Abstract
PurposePresented here is a strategy of sequential lateral decubitus digital subtraction myelography (LDDSM) followed closely by lateral decubitus CT (LDCT) to facilitate cerebrospinal fluid (CSF)-venous fistula (CVF) localization.Materials and MethodsThis is a retrospective analysis of patients referred to our institution for evaluation of CSF leak. Patients with Type 1 and Type 2 leaks, and those not displaying MR brain stigmata of intracranial hypotension were excluded. All patients underwent consecutive LDDSM and LDCT. If the CVF was not localized on the first LDDSM-LDCT pair the patient returned for contralateral examinations. Images were reviewed for CVF and for accumulation of contrast within the renal pelvises expressed as a renal pelvis contrast score (RPCS) in Hounsfield units (HU).ResultsTwenty-two patients were included in this study. In 21 of 22 patients (95%) a CVF was identified yielding an RPCS for the LDDSM-LDCT pair ipsilateral to the CVF ranging from 71 to 423 with an average of 146 HU. An RPCS of the negative side LDDSM-LDCT pair contralateral to a CVF was available in 8 patients and averaged 51 HU. In 4 patients the initial bilateral LDDSM-LDCT pairs did not reveal the location of the CVF however in 3 of these 4 cases the CVF was revealed on a third LDDSM repeated ipsilateral to the higher RPCS.ConclusionThe strategy of sequential LDDSM-LDCT coupled with evaluation of renal accumulation of contrast agent appears to improve the rate of CVF localization and warrants further evaluation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".