MétaCan
Menu
Back to cohort
Record W2950107246 · doi:10.7759/cureus.4966

Percutaneous Pedicle Screws in the Obese: Should the Skin Incision Be More Lateral?

2019· article· en· W2950107246 on OpenAlexaff
Kyle Mombell, Jacob E. Waldron, Patrick B. Morrissey, Nelson S. Saldua

Bibliographic record

VenueCureus · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver Spine Surgery Institute
Fundersnot available
KeywordsMedicinePercutaneousSoft tissueRadiographyLumbarSurgeryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if the skin incision for lumbar percutaneous pedicle screws should be more lateral in the obese patient. METHODS: This was a retrospective radiographic analysis of 30 obese and non-obese lumbar spine computed tomography (CT) radiographs comparing the depth of soft tissue along the anatomic axis of the pedicle at L4 and L5. RESULTS: The average distance from the pedicle trajectory on the skin to the lateral border of the pedicle at L4 was 1.4 cm and 3.8 cm in the non-obese and obese groups, respectively. The average distance from the pedicle trajectory on the skin to the lateral border of the pedicle at L5 was 2.1 cm and 4.3 cm in the non-obese and obese groups, respectively; both these differences reached statistical significance, p <0.05. CONCLUSIONS: This radiographic study supports a more lateral start point for percutaneous pedicle screws in obese patients to maintain an anatomic trajectory when inserting percutaneous pedicle screws into the lumbar spine at L4 and L5. If a skin incision is made at only 1 cm lateral to the pedicle in the obese patient, the surgeon often has to place significant traction on the skin edge to lateralize their instrumentation to achieve an appropriate angle of insertion. By making a more lateral skin incision, less manipulation of the skin and soft tissues is needed to maintain an anatomic trajectory of the pedicle screw. Decreasing soft tissue manipulation may decrease wound and instrumentation complications in this at-risk population.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.702

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)0.0010.000

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 head0.034
GPT teacher head0.341
Teacher spread0.307 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicSpinal Fractures and Fixation TechniquesFrench-language works237,207