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Record W2263919581 · doi:10.1002/adhm.201500793

Biomaterial‐Stabilized Soft Tissue Healing for Healing of Critical‐Sized Bone Defects: the Masquelet Technique

2016· review· en· W2263919581 on OpenAlex
Magdalena Tarchala, Edward J. Harvey, Jake E. Barralet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Healthcare Materials · 2016
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineBone healingSoft tissueSurgery

Abstract

fetched live from OpenAlex

Critical-sized bone defects present a significant burden to the medical community due to their challenging treatment. However, a successful limb-salvaging technique, the Masquelet Technique (MT), has significantly improved the prognosis of many segmental bone defects in helping to restore form and function. Although the Masquelet Technique has proven to be clinically effective, the physiology of the healing it induces is not well understood. Multiple modifiable factors have been implicated by various surgical and research teams, but no single factor has been proven to be critical to the success of the Masquelet Technique. In this review the most recent clinical and experimental evidence that supports and helps to decipher the traditional Masquelet, as well as the modifiable factors and their effect on the success of the technique are discussed. In addition, future developments for the integration of the traditional Masquelet Technique with the use of alternative biomaterials to increase the effectiveness and expand the clinical applicability of the Masquelet Technique are reviewed.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.049
GPT teacher head0.431
Teacher spread0.382 · 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