Minimally Invasive Corrective Osteotomy (MICO) of the Hand a Novel Technique
Bibliographic record
Abstract
Introduction: Patients facing post-traumatic malunion or congenital hand differences often contend with functional and cosmetic issues. Traditional correction methods involve open osteotomy, marked by drawbacks like scarring, non-union risks, prolonged rehabilitation, and adhesions. We therefore introduce a novel minimally invasive technique called Minimally Invasive Corrective Osteotomy of the Hand (MICO), which can be performed under local anesthesia. MICO employs a low-speed, high-torque burr to address finger malunions and congenital anomalies. Case Report: A 49-year-old male patient, generally healthy and right hand dominant, presented with a post-traumatic left middle finger, middle phalanx malunion who underwent the MICO procedure, with a 1-year post-operative follow-up. Conclusion: Our findings suggest that MICO offers a straightforward, reproducible, and delicate solution for correcting hand malunions and congenital finger deformities, potentially mitigating the well-established disadvantages and complications associated with the traditional open approach. Although early results of MICO are promising, a larger case series is needed to evaluate the superiority of this technique compared with current open corrective osteotomy methods.Level of Evidence: IV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".