Rationale and Options for Choosing an Optimal Closure Technique for Primary Midsagittal Osteochondrotomy of the Sternum. Part 3: Technical Decision Making Based on the Practice of Patient- Appropriate Medicine
Bibliographic record
Abstract
The topographic anatomy of the sternum is similar in a healthy population. However, in a clinical subset of patients with comorbidities such as diabetes mellitus, chronic obstructive pulmonary disease, high body mass index, chronic renal disease, or age-related osteoporosis, there are significant changes in the normal physiology that may influence overall patient outcome following trans-sternal intrathoracic surgery. These changes can create technical difficulties in reconstructing the bisected sternum and adversely affect the biomechanics of the thoracic wall, forcing difficult surgical choices with regard to implant options and increasing the cost of an otherwise routine cardiac surgery. A thorough preoperative surgical and technical planning is essential to avert perioperative complications such as failure of wound healing, non-union of the sternum, and life-threatening mediastinitis. Patient expectations need to be explored and the patients should be well informed so that they can make knowledgeable choices regarding their illness and surgical interventions. They should also be given a probable prognosis to provide psychological support. Within the realm of clinical methodology, the concept of patient-appropriate medicine is introduced to direct attending team to become aware of overall health of its patient. The inclusion of a clinical biomechanical engineer as a surgical team member is recommended to perform patient-specific finite element analysis to select an optimal implant to fix the sternum. To help assess the overall benefit-risk profile objectively, an absolute therapeutic index has been proposed.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".