A Comparison and Cost Analysis of Cranioplasty Techniques
Bibliographic record
Abstract
BACKGROUND: Cranioplasty can be performed either with gold-standard, autologous bone grafts and osteotomies or alloplastic materials in skeletally mature patients. Recently, custom computer-generated implants (CCGIs) have gained popularity with surgeons because of potential advantages, which include preoperatively planned contour, obviated donor-site morbidity, and operative time savings. A remaining concern is the cost of CCGI production. The purpose of the present study was to objectively compare the operative time and relative cost of cranioplasties performed with autologous versus CCGI techniques at our center. METHODS: A review of all autologous and CCGI cranioplasties performed at our institution over the last 7 years was performed. The following operative variables and associated costs were tabulated: length of operating room, length of ward/intensive care unit (ICU) stay, hardware/implants utilized, and need for transfusion. RESULTS: Total average cost did not differ statistically between the autologous group (n = 15; $25,797.43) and the CCGI cohort (n = 12; $28,560.58). Operative time (P = 0.004), need for ICU admission (P < 0.001), and number of complications (P = 0.008) were all statistically significantly less in the CCGI group. The length of hospital stay and number of cases needing transfusion were fewer in the CCGI group but did not reach statistical significance. CONCLUSION: The results of the present study demonstrated no significant increase in overall treatment cost associated with the use of the CCGI cranioplasty technique. In addition, the latter was associated with a statistically significant decrease in operative time and need for ICU admission when compared with those patients who underwent autologous bone cranioplasty. LEVEL OF EVIDENCE: IV, therapeutic.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| 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".