Testing biomechanical strength of in vitro cerebrospinal fluid leak repairs.
Bibliographic record
Abstract
OBJECTIVES: Repair of cerebrospinal fluid (CSF) leaks with grafts can be augmented with various adjuncts to improve approximation such as tissue adhesives and sutures. Here we test various adjuncts in an in vitro model of CSF leak repairs. METHODS: A novel pressure testing system was designed to evaluate the burst pressures of in vitro CSF leak repairs. Porcine pericranium grafts were harvested and used to repair a 0.5 X 0.5 cm dural defect. These grafts were sealed in place with no adjunct (control), Tisseel fibrin glue (Baxter, Mississauga, ON), suture, U-CLIPs (a self-closing suture substitute; Medtronic, Toronto, ON), or combined suture and Tisseel. Tisseel samples were tested both as underlay and overlay repairs. Samples were incubated overnight in serum and subjected to burst pressure testing, and pressure-time graphs were recorded. Experiments were conducted five times. RESULTS: Mean burst pressures (measured in pounds per square inch) for grafts sealed in place with Tisseel were significantly higher than all other adjuncts (14.9 Tisseel vs 3.9 control vs 4.1 U-CLIP vs 6.2 suture psi; p < .05). U-CLIPs and sutures did not increase burst strength over controls, and sutures did not have a synergistic effect with Tisseel (12.1 psi). Grafts tested with Tisseel were stronger when tested as underlay than as overlay (14.9 vs 3.0 psi). Three patterns of graft failure were observed based on unique pressure-time graphs. CONCLUSIONS: In vitro burst pressure testing demonstrates that Tisseel improves the strength of CSF leak repairs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".