Development and validation of a preclinical model for training and assessment of cerebrospinal fluid leak repair in endoscopic skull base surgery
Bibliographic record
Abstract
Background Achieving an effective endoscopic skull base reconstruction in case of large dural defects requires specific training and can be extremely challenging. The aim of this study was to describe the development and validation of a preclinical model for cerebrospinal fluid (CSF) leak repair, which can be used for training and to test the mechanical efficacy of endoscopic skull base reconstruction. Methods Eleven fresh‐frozen cadaver heads were dissected. A catheter was inserted in the subdural space via a cervical access, which was sealed with mastic; a vertical graduated tube connected to the catheter measured intracranial pressure (ICP), while stained water was injected intracranially. After endoscopic skull base reconstruction was performed, an expert surgeon assessed its efficacy. ICP was then gradually increased until a leak was evident and CSF leak pressure value was recorded. The correlation between subjective and quantitative evaluations was investigated through Pearson and Spearman correlation tests. Results The model was successfully tested in 11 specimens. A single, large dural defect was created in each model (transplanum‐transtuberculum = 4; transplanum‐transtuberculum‐transsellar = 3; transclival = 3; transcribriform‐transplanum = 1). Skull base reconstruction always comprised a rigid buttress with temporal fascia and/or fat. The CSF leak pressure ranged from 4 to 110 cmH2O. The correlation between expert subjective and quantitative assessment of skull base reconstruction mechanical efficacy was high (r = 0.7; rs = 0.7; p = 0.010 and p = 0.006, respectively). Conclusion This preclinical model is simple, easily reproducible, and effective in simulating an intraoperative leak and objectively measures the CSF leak pressure point of a skull base reconstruction.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".