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Record W2964053347 · doi:10.1016/j.dib.2019.104536

Survey data on the quality of life of consumers fitted with osseointegrated fixation and bone-anchored limb prostheses provided by government organization

2019· article· en· W2964053347 on OpenAlexaboutno aff
Laurent Frossard, Luciann Ferrada, Debra Berg

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
FundersAustralian Government
KeywordsProsthesisOsseointegrationGovernment (linguistics)Fixation (population genetics)MedicineOperations managementEngineeringSurgeryImplant

Abstract

fetched live from OpenAlex

The data in this paper are related to the research article entitled "Development of a government continuous quality improvement procedure for assessing the provision of bone anchored limb prosthesis: A process re-design descriptive study" (Frossard et al., Canadian Prosthetics & Orthotics Journal, 2018. 1(2). p. 1-14). This article contains quality of life data experienced by individuals before and after implantation of a press-fit or screw-type osseointegrated fixation when fitted with conventional socket-suspended and bone-anchored limb prosthesis, respectively. This specifically-designed survey was developed and administered by Queensland Artificial Limb Services (QALS), an Australian State government organization. It was an integrated part of QALS' continuous quality improvement procedure for assessing the provision of bone-anchored prosthesis. A total of 12 out of the 65 consumers completed to the survey, giving a return rate of 18%. This benchmark information can contribute to inform the design of (A) other patients' experience surveys including those built-in governmental continuous quality improvement procedure as well as (B) clinical trials looking at the overall effects of surgical implantation of ossoeintegrated fixation on patients' quality of life. Online repository contains the files: https://data.mendeley.com/datasets/bkbxxmrhfh/1.

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)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.047
GPT teacher head0.293
Teacher spread0.246 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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