The Accordion Maneuver: A Noninvasive Strategy for Absent or Delayed Callus Formation in Cases of Limb Lengthening
Bibliographic record
Abstract
The distraction osteogenesis (DO) technique has been used worldwide to treat many orthopaedic conditions. Although successful, absent or delayed callus formation in the distraction gap can lead to significant morbidities. An alternate cycle of distraction-compression (accordion maneuver) is one approach to accelerate bone regeneration. The primary aim of our study is to report our experience with the accordion maneuver during DO and to provide a detailed description of this technique, as performed in our center. The secondary aim is to present a review of the literature regarding the use of accordion maneuver. We reviewed the database of all patients undergoing limb lengthening from the year of 1997 to 2012. Four patients (6.15%) out of 65 showed poor bone regenerate in their tibiae and therefore accordion maneuver was applied for a mean of 6.75 weeks. Of these, three patients have had successful outcome with this technique. The literature showed that this technique is successful approach to trigger bone healing. However, details of how and when to apply this combination of distraction-compression forces were lacking. In conclusion, the accordion technique is safe noninvasive approach to promote bone formation, thus avoiding more invasive surgical procedures in cases of poor callus formation in limb lengthening.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".