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Record W4298076849

Retinal Prosthesis System for Advanced Retinitis Pigmentosa: A Health Technology Assessment Update.

2017· article· en· W4298076849 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsRetinitis pigmentosaArgusVisual prosthesisMedicinePopulationHealth careOptometryOphthalmologyRetinalEnvironmental healthComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Retinitis pigmentosa is a group of inherited disorders characterized by the degeneration of the photoreceptors in the retina, resulting in progressive vision loss. The Argus II system is designed to restore partial functional vision in patients with profound vision loss from advanced retinitis pigmentosa. At present, it is the only treatment option approved by Health Canada for this patient population. In June 2016, Health Quality Ontario published a health technology assessment of the Argus II retinal prosthesis system for patients with advanced retinitis pigmentosa. Based on that assessment, the Ontario Health Technology Advisory Committee recommended against publicly funding the Argus II system for this population. It also recommended that Health Quality Ontario re-evaluate the evidence in 1 year. The objective of this report was to examine new evidence published since the 2016 health technology assessment. METHODS: We completed a health technology assessment, which included an evaluation of clinical benefits and harms, value for money, and patient preferences related to the Argus II system. We performed a systematic literature search for studies published since the 2016 Argus II health technology assessment. We developed a Markov decision-analytic model to assess the cost-effectiveness of the Argus II system compared with standard care, and we calculated incremental cost-effectiveness ratios over a 20-year time horizon. We also conducted a five-year budget impact analysis. Finally, we interviewed people with retinitis pigmentosa about their lived experience with vision loss, and with the Argus II system. RESULTS: Four publications from one multicentre international study were included in the clinical review. Patients showed significant improvements in visual function and functional outcomes with the Argus II system, and these outcomes were sustained up to a 5-year follow-up (moderate quality of evidence). The safety profile was generally acceptable.In the base case economic analysis, the Argus II system was cost-effective compared with standard care if the willingness to pay was more than $97,429 per quality-adjusted life-year. We estimated that funding the Argus II system would cost the province $0.71 to $0.78 million per year over 5 years, assuming 4 implants per year.People with lived experience spoke about the challenges of retinitis pigmentosa, including the gradual but persistent progression of the disease; its impact on their quality of life and their families; and the accessibility challenges they faced. Those who used the Argus II system spoke about its positive impact on their quality of life. CONCLUSIONS: Based on evidence of moderate quality, the Argus II retinal prosthesis system improved visual function, real-life functional outcomes, and quality of life in patients with advanced retinitis pigmentosa. The Argus II system is expensive, but the cost to publicly fund it would be low, because of the small number of eligible patients. The Argus II system can only enable perception of light/dark and shapes/objects, but these advancements represent important gains for people with retinitis pigmentosa in terms of mobility and quality of life.

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)0.016
metaresearch head score (Gemma)0.050
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 score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0130.012
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.003
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 head0.053
GPT teacher head0.308
Teacher spread0.255 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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