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Correction of radius deformity using guided-growth technology in children with multiple hereditary exostoses

2025· article· en· W4408218356 on OpenAlexaff
Aleksandr A. Petel’guzov, Pavel A. Zubkov, Konstantin V. Zherdev, Andrey S. Butenko, Oleg B. Chelpachenko, Yana I. Kavkovskaya, Ivan P. Pimburskiy

Bibliographic record

VenueRussian Pediatric Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsHereditary multiple exostosesDeformityRADIUSMedicineOrthodonticsComputer scienceSurgery

Abstract

fetched live from OpenAlex

Introduction. Multiple hereditary exostoses (MHE) is a disease that progresses as a child grows, which leads to severe deformities of the skeleton. In 30–60% of MHE cases, the bones of the forearms are affected with the development of radius deformity. In addition to resection of bone and cartilage formations, surgical treatment may include various types of osteotomies using submersible metal fixators and external fixation devices. The development of guided-growth technology dictates the need to expand the scope of minimally invasive surgery. The possibilities of using this technology for deformities of the upper extremities have not yet been sufficiently studied. Objective. To evaluate the effectiveness of the method of controlled bone growth in the surgical treatment of ulnar deformity against the background of multiple hereditary exostoses. Materials and methods. In the period from 2021–2024, thirty three 5 to 17 years children (55 segments/forearms) were hospitalized at the National Medical Research Center for Children’s Health. The study group consisted of 13 children (15 segments) diagnosed with: Distal radius deformiry on the background of multiple hereditary exostoses. A control group of 20 (40 segments) children were diagnosed with juvenile idiopathic arthritis. Patients from the study group underwent surgical treatment of radius deformity using temporary arrest of the growth zone (hemiepiphysiodesis) of the radius. All the studied patients were assessed the angle of the ulnar tilt (UT) and of the lunate subsidence (LS) on X-rays of the forearm in direct projection, patients from the study group were radiographed in the preoperative period and 12–20 months after surgery. The statistical analysis of the studied parameters is based on the search for statistical differences in groups and was performed in the Stattech program. Results. The preoperative value of the elbow angle UT was 35° (33.40), after surgical treatment, UT was 27° (24.32) at p = 0.0002. The value of the LS before surgery was 8.73 ± 3.33 mm, after surgical correction LS was 7.48 ± 3.36 mm at p = 0.005. The achieved level of correction of UT and LS in the study group did not statistically differ from that of the control group, which indicates that the target values were achieved. The average angle of correction of the UT parameter was 8°/year, the trend in postoperative LS values was 1.25 mm/year. Conclusion. The technology of guided-growth effectively corrects the axis of the radius deformity in MHE children during growth. The timely application of this technology makes it possible to prevent the development of severe deformity of the radius and to abandon the performance of corrective osteotomies in the future

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.254
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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