Analyzing the Cost of Autogenous Cranioplasty Versus Custom-Made Patient-Specific Alloplastic Cranioplasty
Bibliographic record
Abstract
PURPOSE: Comparing expenses related to autogenous cranial vault reconstruction versus custom-made patient-specific alloplastic cranioplasty. METHODS: The authors retrospectively reviewed charts of a group of patients who underwent autogenous cranioplasty and poly-ether-ether ketone (PEEK) cranioplasty. The data collected from the patient files included demographic information, details of the surgery, postoperative recovery data, and also duration of surgery. The authors also added costs related to the length of surgery, utilization of intensive care unit, length of hospital stay, amount and seriousness of complications, and hardware cost. The outcomes were studied in terms of skull form maintenance and complications.Eleven of our patients had PEEK cranioplasty at Sunnybrook Hospital, Toronto, ON, in the period from July 2009 to June 2011. The authors identified 11 patients who had split skull autogenous bone graft cranioplasty. They were matched for age and skull defect size.Comparable information was collected for both patient groups. The information was examined to compare costs of custom-made patient-specific alloplastic implants and costs of autogenous cranioplasty. RESULTS: Conclusions made from this paper will hopefully serve as guidance for allocation of hospital funding and resources at the Ministry of Health level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".