Cost-effectiveness analysis : fibrin glue versus sutures for conjonctival fixation during pterygion surgery.
Bibliographic record
Abstract
OBJECTIVE: Pterygium surgery requires the removal of pterygium tissue and repair of the conjunctiva with either sutures or fibrin glue. The literature suggests that the cost of fibrin glue could be compensated by reducing procedure time and be more cost-effective. However, to our knowledge, no formal studies have examined this hypothesis. METHOD: Retrospective chart review of patients who received pterygium surgery with only sutures between January 2008 and January 2010, and those whose surgeons used fibrin glue with or without sutures, between April 2017 and November 2018. Equipment cost, operating room (OR) maintenance, and surgeon's remuneration were compared between the groups. RESULTS: A total of 164 eyes were included. Three different procedure methods were noted: use of sutures only, combination of sutures and fibrin glue, or application of fibrin glue alone. The equipment cost was $97, $169.50, and $152.10 for the suture group, dual method, and fibrin-only method. Average procedure time was 35.8 minutes for the sutures-only group, 21.1 minutes for the dual method, and 25.6 minutes for the method using only glue. OR maintenance cost was $51.20 CAD per minute. The total cost for the method using only sutures was $2528.90, whereas the average cost for the protocol using only fibrin glue was $2063. CONCLUSION: Although using fibrin glue for conjunctival graft adhesion increases the equipment cost, it significantly decreases procedure time, which allows a reduction of the total surgery cost. Therefore, fibrin glue is a more cost-effective approach than sutures alone.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".