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Record W4280620894 · doi:10.31399/asm.cp.smst2022p0079

Combination of Solid and Porous Nitinol Implants in Surgical Treatment of Extensive Post-Excision Thoracic Defects in Cancer Patients

2022· article· en· W4280620894 on OpenAlexaff
E. B. Topolnitskiy, Timofey Chekalkin, Екатерина Марченко, Yu. F. Yasenchuk, Jihoon Kang, L’Hocine Yahia

Bibliographic record

VenueConference proceedings from the International Conference on Shape Memory and Superelastic Technologies · 2022
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMedicineSurgeryMediastinumThoracic wallLesionRadiology

Abstract

fetched live from OpenAlex

Abstract Radical surgical intervention for chest wall tumors is typically accompanied by the lesion of osteochondral structures and the appearance of complex post-resection defects, which result in functional and aesthetic impairment. After extensive resection of the chest wall, it is vitally important that it be simultaneously repaired, including restoring the osteochondral framework and the integrity of the integumentary tissues as well as maintaining the anatomical and physiological volume of the mediastinum and the pleural cavities. Porous and solid Nitinol implants and their successful deployment in surgical treatments have encouraged insights for immediate and delayed osteoplasty in cancer patients. The novel aspect of this work consists in this surgical method of post-excision defect repair is performed using a proprietary approach and customized NiTi-based implants. The results indicate that the suggested surgical approach and tactics using one-step repair are one of the promising techniques even though the case is aggravated with extensive chest wall lesions. The approach can be performed safely and can be recommended as a routine procedure with a high success rate. Combined Nitinol implants seem to be very good reinforcing biomaterials that enabled the reliable repair of thoracic post-excisional defects of various sizes with good functional, clinical, and cosmetic outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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