MétaCan
Menu
Back to cohort
Record W4287405278

Stance-Control Knee-Ankle-Foot Orthoses for People With Knee Instability: A Health Technology Assessment.

2021· review· en· W4287405278 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2021
Typereview
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical medicine and rehabilitationPhysical therapyMedicineAnkleGrading (engineering)Economic evaluationSurgeryEngineering
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Knee instability can arise from various causes and conditions such as neuromuscular disease, central nervous system conditions, and trauma. For people with knee instability, knee orthosis devices are prescribed to help with standing, walking, and performing tasks. We conducted a health technology assessment of stance-control knee-ankle-foot orthoses (SCKAFOs) for people with knee instability, which included an evaluation of the effectiveness, safety, and budget impact of publicly funding SCKAFOs, as well as patient preferences and values. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Risk of Bias in Nonrandomized Studies (RoBANS) tool and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search and also analyzed the budget impact of publicly funding SCKAFOs in people with knee instabilities in Ontario. We did not conduct a primary economic evaluation as there was limited comparative clinical evidence to inform an economic model. Our reference case budget impact analysis was done from the perspective of the Ontario Ministry of Health; it compared the total costs of a basic mechanical SCKAFO and locked KAFO (LKAFO) for people with knee instability. We also performed scenario analyses varying the following parameters: the price of all classes of SCKAFO (mechanical, electronic, and microprocessor), and the uptake of SCKAFO. To contextualize the potential value of SCKAFO, we spoke with people with knee instability. RESULTS: We included four studies in the clinical evidence review. We are uncertain if SCKAFOs improve walking ability, energy consumption, or activities of daily living compared with LKAFOs (GRADE: Very low). Our economic evidence review identified one costing analysis that suggested that the costs of orthotic devices such as LKAFOs and SCKAFOs are highly variable according to the cost of materials, professional time, and customization required by the individual patient. The budget impact of publicly funding mechanical SCKAFOs in Ontario over the next 5 years (at a full device cost of $10,784) ranged from an additional $0.50 million in year 1 (at an uptake rate of 30% in the target population [429 eligible people]) to $0.83 million in year 5 (at an uptake rate of 50%), with a total budget impact of $3.34 million over 5 years. We found that the greatest increase in budget impact in the scenario analysis came from the microprocessor SCKAFO device, which had an additional cost of $10.07 million in year 1, increasing to $16.78 million in year 5. When we decreased the cost of a mechanical SCKAFO device (to $7,384), this reduced the 5-year budget impact to $0.89 million (vs. $3.34 million in the reference case). The people with knee instability with whom we spoke reported that they preferred a device that would provide a more typical gait, but starting with this type of device would be easier than switching from an existing LKAFO. CONCLUSIONS: We are uncertain if SCKAFOs improve walking ability, reduce energy consumption, or improve activities of daily living compared with LKAFOs. We estimate that the additional cost to provide public funding for a mechanical SCKAFO in people with knee instability would range from about $0.50 million in year 1 to $0.83 million in year 5, yielding a total budget impact of $3.34 million over 5 years. Depending on the class of SCKAFO and the uptake rate for the device, the budget impact may vary. People who met the criteria for the use of a SCKAFO did have a strong preference for it over an LKAFO.

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)0.033
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.067
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.087
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0130.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207