Survey data on the quality of life of consumers fitted with osseointegrated fixation and bone-anchored limb prostheses provided by government organization
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".