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Record W4225378600 · doi:10.1097/gox.0000000000004233

The Modified Sternoplasty: A Novel Surgical Technique for Treating Mediastinitis

2022· article· en· W4225378600 on OpenAlexaff

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2022
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsYork University
Fundersnot available
KeywordsMediastinitisPectoralis major muscleRib cageDebridement (dental)Axillary linesPectoralis MuscleCardiac surgeryMortality rate

Abstract

fetched live from OpenAlex

Deep sternal wound infection (DSWI) is one of the most complex and devastating complications post cardiac surgery. We present here the modified sternoplasty, a novel surgical technique for treating DSWI post cardiac surgery. The modified sternoplasty includes debridement and sternal refixation via bilateral longitudinal stainless-steel wires that are placed parasternally along the ribs at the midclavicular or anterior axillary line, followed by six to eight horizontal stainless-steel wires that are anchored laterally and directly into the ribs. On top of that solid structure, wound reconstruction is performed by the use of bilateral pectoralis muscle flaps followed by subcutaneous tissue and skin closure. We reported mortality rates and length of hospitalization of patients who underwent the modified sternoplasty. In total, 68 patients underwent the modified sternoplasty. Two of these critically ill patients died (2.9%). The average length of hospitalization from the diagnosis of DSWI was 24.63 ± 22.09 days. The modified sternoplasty for treating DSWI is a more complex surgery compared with other conventional sternoplasty techniques. However, this technique was demonstrated to be more effective, having a lower rate of mortality, and having a length of hospitalization lower than or comparable to other techniques previously reported in the literature.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.312
Teacher spread0.271 · 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 designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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