The “Crossing Collection Sign”: A Diagnostic Tool on Spine Magnetic Resonance Imaging For Localizing Cerebrospinal Fluid Leak
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to determine whether the site of "cross" between ventral and dorsal spinal longitudinal extradural CSF collections (SLECs) seen on magnetic resonance imaging during initial workup of patients with suspected CSF leaks can predict the subsequently confirmed leakage site on computed tomography myelography or surgical repair. METHODS: This was an institutional review board-approved, retrospective study performed from 2006 to 2021. Patients with SLECs who underwent total spine magnetic resonance imaging at our institution, followed by myelography and/or surgical repair for CSF leak, were included. Patients with incomplete workup including lack of computed tomography myelography and/or surgical repair and patients severely motion degraded imaging were excluded from our study. The site of cross between ventral and dorsal SLECs was defined as the "crossing collection sign" and was compared with the anatomically confirmed site of leak on myelography and/or at surgical repair. RESULTS: Thirthy-eight patients met inclusion criteria with 18 females and 11 males ranging in age from 27 to 60 years (median, 40 years; interquartile range, 14 years). The crossing collection sign was seen in 76% of patients (n = 29). The distributions of confirmed CSF leak were as follows: cervical (n = 9), thoracic (n = 17), and lumbar spine (n = 3). The crossing collection sign predicted the site of CSF leak in 14 of 29 patients (48%) and was within 3-vertebral segments in 26 of 29 cases (90%). CONCLUSIONS: The crossing collection sign can help prospectively identify spinal regions with highest likelihood for CSF leak in patients with SLECs. This can potentially help optimize the more invasive subsequent steps in the workup for these patients, including dynamic myelography and surgical exploration for repair.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".