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Record W2820505478 · doi:10.1097/bot.0000000000001206

Surgical Technique: Anterolateral Approach to the Humerus

2018· article· en· W2820505478 on OpenAlexaff
Tomas Liskutin, Hobie Summers, William D. Lack, Mitchell Bernstein

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2018
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineHumerusSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Although most humeral shaft fractures can be treated nonoperatively, many patients do benefit significantly from surgical treatment. The anterolateral approach to the humerus provides excellent exposure to the humeral shaft, especially to more proximal aspects. In addition, the approach can be extended both proximally and distally, providing the surgeon a dynamic exposure to the humerus for the treatment of fractures and other pathologies. METHODS: This video highlights a clinical case where a mid-shaft humerus fracture was diagnosed and treated with open reduction internal fixation using an 8-hole 4.5-mm limited contact dynamic compression plate through an anterolateral approach. RESULTS: We present indications, anatomic considerations, and surgical techniques used to surgically treat a transverse, mid-shaft humerus fracture through an anterolateral approach. In addition, we demonstrate the use of a push-pull screw to aid in achieving appropriate compression across a fracture site. Using this technique, an anatomic reduction and satisfactory clinical outcome were achieved. CONCLUSIONS: The anterolateral approach to the humerus offers excellent exposure to some humeral shaft fractures, particularly those that lie more proximally. However, its use must be weighed carefully against several other approach options, and the surgeon must account for the specific fracture pattern, as well as their own comfort and familiarity with the approach.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.278

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 head0.021
GPT teacher head0.294
Teacher spread0.273 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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