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Osteosíntesis con placa helicoidal en las fracturas de fémur distal

2022· article· es· W4224283519 on OpenAlexaboutno aff
Agustín Quesada, Fabricio Videla Ávila, Gastón Horué Pontoriero, Jorge Eduardo Filisetti

Bibliographic record

VenueRevista de la Asociación Argentina de Ortopedia y Traumatología · 2022
Typearticle
Languagees
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Introducción: En pacientes con fracturas de fémur distal con alto grado de conminución metafisaria (AO 33 C2, C3) y fracturas periprotésicas (Vancouver tipo C), el uso de doble osteosíntesis brinda mayor estabilidad a la fractura. Las placas helicoidales se emplean cada vez más con el objetivo de evitar dañar elementos nobles relacionados con el abordaje. Materiales y Métodos: Entre 2017 y 2021, seis pacientes fueron tratados mediante osteosíntesis con doble placa (por vía lateral y helicoidal por vía medial). La serie se compone de cuatro mujeres y dos hombres. El 66% (4 pacientes) tenía fracturas de fémur distal, y el resto (33%), fracturas periprotésicas tipo Vancouver C. Resultados: Se observó la consolidación radiográfica en todos los pacientes, a los 6 meses de la cirugía, con retorno normal a la actividad previa. Ninguno sufrió una lesión vasculonerviosa asociada. Conclusiones: La placa helicoidal es una gran opción para las fracturas de fémur distal con conminución y las fracturas femorales periprotésicas tipo Vancouver C. Esto demuestra que, aplicando los principios básicos de osteosíntesis, con una técnica sencilla, se pueden suplir materiales más sofisticados, y obtener resultados radiográficos similares.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.281
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.

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

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