In-silico patient-specific and patient-appropriate engineering method to judiciously select an ameliorative implant design in a single-patient using finite element-n-of-1 (fe-n-of-1) empirical test analysis to reconstruct mid-sagittal osteochondrotomy of the sternum following cardiac surgery
Bibliographic record
Abstract
Introduction: No two patients have similar normal anatomy and physiology because of genetics, physical development, and age that the same type of surgery and reconstruction implant will perform equally well. Such a notion demands the need for individualization of treatment and a method to select an ameliorative implant prospectively. One such empirically testing method is the finite element-n-of-1 (fe-n-of-1), where a treatment plan is executed specifically and systematically for a single patient as part of pre-operative planning. Objective: It is to evaluate and discuss the method of finite element analysis to carry out the fe-n-of-1 empirical test in a fact-driven manner connecting various scientific domains. It presents a preliminary protocol how to select an ameliorative implant to mitigate sternal instability due to suboptimal standard stainless-steel cerclage wiring to reconstruct the sternum following open-heart surgery. Methodology: The instability following the reconstruction of the sternum is a mechanical problem therefore it is appropriate to apply harmless structural engineering methods to choose a suitable implant design to fix it. This exploratory descriptive research describes finite element n-of-1 empirical testing using in-silico engineering principles applied to patient-specific and patient-appropriate mechanical loading conditions. Conclusion: Single-patient fe-n-of-1 empirical testing is a benign engineering method based on finite element modeling and finite element analysis. It is a safe mathematical evaluation free from subjective bias to select in advance the most ameliorative implant design to opt out of the suboptimal stainless steel cerclage wire as ‘standard of care’ and improve patient-based outcome and surgeon satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.000 | 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 teacher head, 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".