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Record W4387740653 · doi:10.1177/23814683231199721

Assessing an Online Patient Decision Aid about Upper Extremity Reconstructive Surgery for Cervical Spinal Cord Injury: Pilot Testing Knowledge, Decisional Conflict, and Acceptability

2023· article· en· W4387740653 on OpenAlexaboutno aff
William R. Moritz, Amanda M. Westman, Mary C. Politi, DOD Working Group, Ida K. Fox

Bibliographic record

VenueMDM Policy & Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsnot available
FundersCongressionally Directed Medical Research ProgramsStanford Bio-XCurtin University of TechnologyU.S. Department of Defense
KeywordsMedicineSpinal cord injuryRehabilitationPhysical therapyPopulationQualitative researchPhysical medicine and rehabilitationSpinal cordPsychiatry

Abstract

fetched live from OpenAlex

Background. While nerve and tendon transfer surgery can restore upper extremity function and independence after midcervical spinal cord injury, few individuals (∼14%) undergo surgery. There is limited information regarding these complex and time-sensitive treatment options. Patient decision aids (PtDAs) convey complex health information and help individuals make informed, preference-consistent choices. The purpose of this study is to evaluate a newly created PtDA for people with spinal cord injury who are considering options to optimize upper extremity function. Methods. The PtDA was developed by our multidisciplinary group based on clinical evidence and the Ottawa Decision Support Framework. A prospective pilot study enrolled adults with midcervical spinal cord injury to evaluate the PtDA. Participants completed surveys about knowledge and decisional conflict before and after viewing the PtDA. Acceptability measures and suggestions for further improvement were also solicited. Results. Forty-two individuals were enrolled and completed study procedures. Participants had a 20% increase in knowledge after using the PtDA ( P < 0.001). The number of participants experiencing decisional conflict decreased after viewing the PtDA (33 v. 18, P = 0.001). Acceptability was high. To improve the PtDA, participants suggested adding details about specific surgeries and outcomes. Limitations. Due to the COVID-19 pandemic, we used an entirely virtual study methodology and recruited participants from national networks and organizations. Most participants were older than the general population with a new spinal cord injury and may have different injury causes than typical surgical candidates. Conclusions. A de novo PtDA improved knowledge of treatment options and reduced decisional conflict about reconstructive surgery among people with cervical spinal cord injury. Future work should explore PtDA use for improving knowledge and decisional conflict in the nonresearch, clinical setting. Highlights People with cervical spinal cord injury prioritize gaining upper extremity function after injury, but few individuals receive information about treatment options. A newly created patient decision aid (PtDA) provides information about recovery after spinal cord injury and the role of traditional tendon and newer nerve transfer surgery to improve upper extremity upper extremity function. The PtDA improved knowledge and decreased decisional conflict in this pilot study. Future work should focus on studying dissemination and implementation of the ptDA into clinical practice.

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)0.010
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.163
GPT teacher head0.469
Teacher spread0.306 · 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 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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