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Sacral U-type Fractures in Patients Older Than 65 years

2024· article· en· W4403588628 on OpenAlexaff
Avrey Novak, Joseph T. Patterson, Michael Githens, Reza Firoozabadi, Conor P. Kleweno

Bibliographic record

VenueJAAOS Global Research and Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineAssistive deviceDemographicsLow energyHigh energyActivities of daily livingPhysical therapyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this study was to determine the degree of disability that geriatric patients with sacral U-type fractures experience. METHODS: Patients older than 65 years presenting from 2013 to 2019 with a U-type sacral fracture were included. Patient demographics, treatment type, preinjury domicile, preinjury use of assistive devices, and neurologic deficits were recorded. Outcomes included mortality, return to preinjury domicile, and use of assistive devices for mobility. RESULTS: Among 46 patients in the treatment period, ground-level fall was the most common mechanism of injury (60.8%). Thirty-four patients (74%) were treated surgically, most commonly with closed percutaneous fixation (n = 27). Thirteen percent of patients died during the admission. At the final follow-up, 14 (45%) had not returned to their prior domicile and 18 (58%) required more supportive assistive devices. Seventy-three percent of patients who presented delayed required a new gait aid, compared with 47% presenting acutely. Between those presenting with low-energy versus high-energy mechanisms, similar rates of need were observed for new assistive devices (50% low and 73% high) and lack of return to preinjury domicile (40% low, 50% high). DISCUSSION: Many geriatric patients were disabled by or died after sustaining a sacral U-type fracture, highlighting the morbidity regardless of high-energy or low-energy trauma.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.448
Teacher spread0.394 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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