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AB0891 PROOF OF CONCEPT OF THE USE OF CO-DESIGNED 3D PRINTED ASSISTIVE DEVICES WITHIN AN OCCUPATIONAL THERAPY INTERVENTION IN SYSTEMIC SCLEROSIS

2023· article· en· W4379647391 on OpenAlexaboutno aff
Amelia Spinella, Francesco Gherardini, Enrico Dalpadulo, D. Giuriati, Valentina Bettelli, Marco de Pinto, G. Amati, O. Secchi, Maria Teresa Mascia, Gilda Sandri, Dilia Giuggioli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntervention (counseling)PsychosocialOccupational therapyAbandonment (legal)Assistive technologyAssistive devicePhysical therapyPhysical medicine and rehabilitationNursingComputer scienceHuman–computer interactionPsychiatry

Abstract

fetched live from OpenAlex

Background Despite the positive impact of assistive devices (ADs) on the daily lives of people with disabilities, many of them are initially adopted and then regrettably abandoned. Even though the AD of an individual patient might fit his/her needs, up to 7 out of 10 people stop using it. On the one hand this may be linked to an improvement in their health, on the other, the devices may not be ergonomically suitable for long-term use. Previous studies have highlighted the possibility that co-designed and customised 3D printed assistive devices can benefit patients with rheumatic diseases which impair daily activities, especially manual ones. Among the 29 patients with Systemic Sclerosis who took part in the joint prevention clinic workshop at the University-Hospital of Modena (Italy), in 2019 4 patients actively joined the 3D printing project for the co-designing of ADs. Objectives This work has two main goals: firstly, to check whether in the short term the co-design approach is able to guarantee better acceptance of ADs and a lower abandonment rate. Secondly, to check during follow-ups, if the patients are satisfied and regularly use their ADs and if their daily activities remain unchanged. Methods The development of the ADs begins with co-design sessions which involve the patient, an Occupational Therapist and a designer. The AD is digitally modelled and 3D printed. Subsequently it is delivered to the patient following a number of training sessions. Using standardized tests such as PIADS (Psychosocial Impact of Assistive Devices Scale) and QUEST (Quebec User Evaluation of Satisfaction with assistive Technology) at the time of delivery of the ADs, then after 3 years, we assessed the level of satisfaction of the 4 patients enrolled, the condition of the ADs and their actual use. Results In 2019 each of the 4 patients received at least one AD, which they used on a regular daily basis [i.e. a device to open a moka pot (an Italian coffee machine); a token for a shopping trolley; a pen grip handle; a multiple key turner]. The results of the PIADS following delivery and in the 3-years follow-up were positive. The QUEST results were also positive. After 3 years the ADs were still being used regularly. Moreover, in 2022, 2 patients co-designed and 3D printed new ADs in response to new or altered needs. Conclusion In both the long and short term, ADs are still being used regularly and patients are still enjoying the benefits. After 3 years none of the ADs displayed any significant wear or breakage, thus their effectiveness was not compromised. Finally, the satisfaction of the patients with the co-designed ADs remained unaltered over time. Co-design 3D printed devices have therefore proven to effectively reduce the AD abandonment rate. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests None Declared.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.144
GPT teacher head0.335
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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