Use of Tissue Glues in Endoscopic Pituitary Surgery: A Cost Comparison
Bibliographic record
Abstract
BACKGROUND: Post-operative cerebrospinal fluid (CSF) leaks are a common complication of endoscopic pituitary surgery and account for a significant proportion of hospital costs associated with this procedure. Tisseel® is a tissue glue commonly used as an adjunct in dural repair but is not optimal for this purpose. DuraSeal® has several properties advantageous for dural repair but is not widely accepted in Canada partly due to its increased cost. OBJECTIVE: A cost analysis of DuraSeal® versus Tisseel® in endoscopic pituitary surgery. METHODS: A cost analysis was performed based on typical endoscopic pituitary surgery cases performed at our tertiary care institution. Operating room, hospital admission, and surgical sealant costs were obtained directly while estimates of patient recovery time and post-operative CSF leak rates were based on consensus values reported in the literature. Outcomes were reported for various possible clinical scenarios of sealant use. RESULTS: In a model where surgical sealant is employed only in high-risk cases, use of DuraSeal® allows for a yearly cost savings of at least $4486.72. If surgical sealant is used in all cases, regular use of DuraSeal® versus Tisseel® either marginally reduces yearly costs or increases them by a maximum of $7619.25, depending on the case volume and estimated post-operative CSF leak rate. CONCLUSION: In most clinical scenarios, use of DuraSeal® in endoscopic pituitary surgery may reduce overall yearly hospital costs compared to Tisseel®.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".