Comparative Cost-Effectiveness of Cranioplasty Implants
Bibliographic record
Abstract
Purpose: The aim of this study was to compare operative duration and total hospital costs incurred for patients undergoing elective cranioplasty with a variety of materials, including manually shaped autogenous bone graft and titanium mesh, custom patient-specific titanium mesh, polymethyl methacrylate (PMMA) acrylic, and polyetheretherketone (PEEK) implants. Methods: A single-centre retrospective chart review was used. Patient demographics, defect characteristics, total operative time, and length of hospital stay were obtained. Total costs were sourced from Sunnybrook and standardized to the 2014 to 2015 year. Bivariate and age-controlled multivariate analyses were performed with (n = 119) and without (n = 101) outliers. Results: When outliers were removed, an age-controlled analysis revealed that autogenous implants resulted in an operative time of 178 ± 37 minutes longer than manually shaped titanium implants ( P < .01). The average cost of cranioplasty was CAD$18 335 ± CAD$10 265 for manually shaped titanium implants, CAD$31 956 ± CAD$31 206 for custom patient-specific titanium implants, CAD$20 786 ± CAD$13 075 for PMMA, CAD$14 291 ± CAD$5562 for autogenous implants, and CAD$27 379 ± CAD$4945 for PEEK implants ( P = .013). When outliers were removed, cranioplasty with PMMA and PEEK incurred greater costs, CAD$4442 ± CAD$2100 and CAD$13 372 ± CAD$2728, respectively, more than manually shaped titanium implants ( P < .01). Conclusions: Manually shaped titanium mesh is the most cost-effective implant choice for small cranial defects. Large unknown defects and frontal paranasal sinus defects are most effectively treated with autogenous bone or titanium mesh. Despite prolonged operative duration and inpatient admission, total costs were not significantly increased. Both PMMA and PEEK implants were significantly more costly, which may be a result of higher complications necessitating reoperation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".