Complications associated with talectomy in paediatric patients: a comparative retrospective study of two surgical techniques
Bibliographic record
Abstract
BACKGROUND: Studies describing the surgical approaches utilized for talectomy and their associated complications are scarce. We aimed to compare the surgical techniques and associated procedures from two groups of patients who underwent talectomy using two approaches. The main purpose of this study was to describe the complications and recurrence rates associated with each technique. METHODS: Between January 2004 and December 2019, 62 talectomies were performed in 48 pediatric patients with different pathologies. All patient data were reviewed retrospectively, and data of 31 patients were included in the study. The patients were divided into two groups based on the surgical technique used, and the baseline characteristics, along with the post-operative findings, and the intervention types in relation to complications were analyzed. RESULTS: In the terms of hindfoot varus, midfoot adductus, forefoot supination, and dorsal bunions, the prevalence of these deformities was higher in group (A). Group (B) patients tolerated braces (88.9 %) better than group (A) patients (84.0 %). More adjunct procedures were required in group (A) than group (B) Furthermore, the frequency and types of complications, as well as the need for further surgeries were also higher in group (A). There was a higher rate of recurrence in group A than group B. CONCLUSIONS: Talectomy is an effective procedure for the treatment of persistent foot deformities despite associated complications. Surgical details and addressing associated deformities with adjunct surgical interventions should be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".