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Record W4405632613 · doi:10.2106/jbjs.cc.24.00300

Prevention of Stump Overgrowth in Pediatric Patients with Traumatic Crush Injury of the Leg

2024· article· en· W4405632613 on OpenAlexaff
Gourav Jandial, Terence Kwan‐Wong, Christiaan J. A. van Bergen, Kishore Mulpuri, Anthony Cooper

Bibliographic record

VenueJBJS Case Connector · 2024
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineCrush injurySurgeryAmputationComplicationSoft tissueFoot (prosody)

Abstract

fetched live from OpenAlex

CASE: Stump overgrowth is the most common complication in skeletally immature amputees. Various techniques including capping the amputated stump have been used to prevent it but have been associated with variable rates of recurrence of bony overgrowth. We report a technique of intercalary tibial shortening prophylactically to avoid stump overgrowth in some specific situations in children with traumatic crush injuries of the leg/foot. CONCLUSION: The technique described in this case report not only provides more soft tissue coverage but also at 1-year follow-up, successfully prevented one of the most frequent complications seen in pediatric post-traumatic amputation, the stump overgrowth.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 designCase report
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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