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Record W4255172605 · doi:10.1097/prs.0000000000000526

Microtia Reconstruction

2014· review· en· W4255172605 on OpenAlex

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

VenuePlastic & Reconstructive Surgery · 2014
Typereview
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsMisericordia Community Hospital
FundersCore Research for Evolutional Science and Technology
KeywordsMicrotiaMedicineRehabilitationSelection (genetic algorithm)Medical physicsSurgeryComputer sciencePhysical therapyArtificial intelligence

Abstract

fetched live from OpenAlex

LEARNING OBJECTIVES: After reviewing this article, the participant should be able to understand: 1. The epidemiology and genetics of microtia. 2. Refinements in surgical technique for microtia. 3. Outcomes of treatment. 4. Challenges in treatment selection, hearing restoration, surgical training, and tissue engineering. SUMMARY: Microtia reconstruction is both challenging and controversial. Our understanding of the epidemiology and genetics of microtia is improving. Surgical techniques continue to evolve, with better results. Treatment selection continues to be controversial. There are strong proponents for reconstruction with costal cartilage, Medpor or a prosthesis. More realistic models for teaching surgeons how to do the procedures are becoming available. Our approach to hearing rehabilitation is changing. Better solutions using percutaneous and implantable devices are under evaluation to help both unilateral and bilateral microtia patients. Tissue engineering will offer some exciting new treatment possibilities 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.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.003
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.311
Teacher spread0.270 · 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