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Record W2018729874 · doi:10.4081/std.2011.e20

Traumatic Ribs Fracture: How to Treat Them?

2011· article· en· W2018729874 on OpenAlexaff
Marco Scarci, Andrea Billè, Imran Zahid, Tom Routledge

Bibliographic record

VenueSurgical Techniques Development · 2011
Typearticle
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsFlail chestMedicineRib cageInternal fixationSurgeryCLIPSFixation (population genetics)Damage controlAnatomy

Abstract

fetched live from OpenAlex

Flail chest complicates about 10% of patients with chest trauma and is associated with a mortality rate of 10–20% in older series, while a recent one reports no mortality. The majority of the cases are treated conservatively with internal pneumatic stabilization and pain control. In recent years, nevertheless, we assisted in the resurgence of chest wall fixation due to the availability of new devices. We report our experience in the use of mouldable titanium clips (STRACOS, Strasbourg Thoracic Osteosyntheses System; MedXpert, Heitersheim, Germany) to fix traumatic rib fracture. This device presents an advantage over previous strategies, as it is easy to apply and doesn’t require drilling and screwing of the ribs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.999

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)0.0020.000

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 head0.089
GPT teacher head0.288
Teacher spread0.199 · 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 designNot applicable
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
Published2011
Admission routes1
Has abstractyes

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