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Analysis of Scar Formation After Lower Limb Lengthening

2004· article· en· W4230106315 on OpenAlexaboutno aff
Law Ka Pui Karlen, W Yinusa, Lam Shuk Yan, Kwan Wing Wang, Li Yun Hoi, Leong Chi Yan John

Bibliographic record

VenueJournal of Pediatric Orthopaedics · 2004
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCosmesisScarsSurgeryThigh

Abstract

fetched live from OpenAlex

Limb lengthening aims to reduce limb length discrepancy, improve cosmesis, and permit more functional mobility. Scarring, however, is a major concern of patients. In this study 25 patients (27 lengthening sites) were assessed; 6 sites were lengthened with Orthofix and 21 sites with Ilizarov. Altogether, 452 pin tract scars were assessed. The mean scar length was 11.5 mm in the Ilizarov group (n = 415) and 54.1 mm in the Orthofix group (n = 37). The mean scar width was 5.6 mm in the Ilizarov group and 11.5 mm in the Orthofix group. The mean patient satisfaction was 7.3 in the Orthofix group and 5.5 in the Ilizarov group. The mean Vancouver score was 0.66 in the Ilizarov group and 3.1 in the Orthofix group. The scars were shorter and cosmetically better in the Ilizarov group, but patients were more satisfied in the Orthofix group because there were fewer scars. Patients were also more satisfied with the thigh scars than with the leg scars because clothing can easily cover the thigh scars.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.248
Teacher spread0.239 · 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 teacher head, 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".

Quick stats

Citations11
Published2004
Admission routes1
Has abstractyes

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