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Record W2995232140 · doi:10.48550/arxiv.1912.10601

Optimized Cranial Bandeau Remodeling

2019· preprint· en· W2995232140 on OpenAlexaff
James M. Drake, Marina Drygala, Ricardo Fukasawa, Jochen Koenemann, André Linhares, Thomas Looi, John H. Phillips, David Qian, Nikoo R. Saber, Justin Toth, Chris Woodbeck, Jessie Yeung

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Craniosynostosis, a condition affecting 1 in 2000 infants, is caused by premature fusing of cranial vault sutures, and manifests itself in abnormal skull growth patterns. Left untreated, the condition may lead to severe developmental impairment. Standard practice is to apply corrective cranial bandeau remodeling surgery in the first year of the infant's life. The most frequent type of surgery involves the removal of the so-called fronto-orbital bar from the patient's forehead and the cutting of well-placed incisions to reshape the skull in order to obtain the desired result. In this paper, we propose a precise optimization model for the above cranial bandeau remodeling problem and its variants. We have developed efficient algorithms that solve best incision placement, and show hardness for more general cases in the class. To the best of our knowledge this paper is the first to introduce optimization models for craniofacial surgery applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.214
Teacher spread0.129 · 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.

Study designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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