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Record W4220911815 · doi:10.52312/jdrs.2022.372

Evaluation of the patient satisfaction of using a 3D printed medical casting in fracture treatment

2022· article· en· W4220911815 on OpenAlexaboutno aff
Serkan Sürücü, Mahmud Aydın, Ahmet Güray Batma, Deniz Karaşahin, Mahir Mahiroğulları

Bibliographic record

VenueJoint Diseases and Related Surgery · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishJoint (building)Foundation (evidence)MedicineOpen fractureCastingPatient satisfactionSurgeryEngineeringPolitical scienceArtVisual artsOrthopedic surgeryLawCivil engineering

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aims to assess, through a questionnaire, the functionality, and efficacy of using three-dimensional (3D) printed medical casts. PATIENTS AND METHODS: Between February 2017 and March 2019, a total of 24 patients (14 males, 10 females; mean age: 33.1±9.4 years, range, 12 to 62 years) with upper extremity fracture who were applied 3D printed medical cast were included. Patient satisfaction was evaluated using the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST 2.0). Each item is scored on a five-point scale. RESULTS: The mean follow-up was 14 (range, 6 to 18) months. All fractures healed within four to six weeks without any complications. In all cases, there was no loss of reduction. The total mean QUEST 2.0 satisfaction score for the participants was 4.7. The ratings on each scale ranged from 4.5 to 4.9. CONCLUSION: Almost all patients with upper extremity fractures were satisfied with the 3D printed medical cast. The patients found the 3D printed medical cast to be comfortable, safe, easy-to-apply, lightweight, and effective.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.235

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 head0.019
GPT teacher head0.242
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations10
Published2022
Admission routes1
Has abstractyes

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