Suturing Technique and the Integrity of Dural Closures: An in Vitro Study
Bibliographic record
Abstract
OBJECTIVE: The watertight closure of the dura mater is fundamental to intracranial procedures in neurosurgery. Nevertheless, for any given operator and type of suture, it is still not certain which suturing technique affords the most watertight dural closure. We have developed a laboratory model that allows us to compare the pressures at which dural closures leak when different suturing techniques are used. METHODS: Human cadaveric dura was secured to a glass cylinder filled with colored saline. By application of force to a bag of saline attached to the cylinder, the pressure at which sutured dural incisions leak can be recorded. Using this method, we have compared the closure of 2-cm dural incisions with 3-0 silk using the following techniques (10 per group): 1) interrupted simple, 2) running simple, 3) running locked, and 4) interrupted vertical mattress. We have also compared the closure of 1- x 3-cm dural windows with cadaveric dura and 3-0 silk using the same suturing techniques (10 per group). RESULTS: The pressure at which 2-cm linear dural incisions leaked was significantly higher when they were closed with the interrupted simple suturing technique (P < 0.05). There was no significant difference among the different suturing techniques when they were used to close a 1- x 3-cm dural window with a duraplasty. Overall, the pressures at which sutured linear dural incisions leaked were higher than the pressures at which sutured dural windows closed with duraplasties leaked. CONCLUSION: In the experimental model described, an interrupted simple suturing technique affords the most watertight dural closure for linear incisions, whereas no suturing technique proved advantageous for the closure of a duraplasty.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".