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Record W1978694590 · doi:10.1186/1748-5908-5-48

The GRADE approach for assessing new technologies as applied to apheresis devices in ulcerative colitis

2010· article· en· W1978694590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueImplementation Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster University
FundersInstituto de Salud Carlos III
KeywordsMedicineUlcerative colitisApheresisQuality of Life ResearchAbdominal surgeryHealth services researchHealth informaticsPublic healthHealth administrationInternal medicineGeneral surgeryGastroenterologyPathologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: In the last few years, a new non-pharmacological treatment, termed apheresis, has been developed to lessen the burden of ulcerative colitis (UC). Several methods can be used to establish treatment recommendations, but over the last decade an informal collaboration group of guideline developers, methodologists, and clinicians has developed a more sensible and transparent approach known as the Grading of Recommendations, Assessment, Development and Evaluation (GRADE). GRADE has mainly been used in clinical practice guidelines and systematic reviews. The aim of the present study is to describe the use of this approach in the development of recommendations for a new health technology, and to analyse the strengths, weaknesses, opportunities, and threats found when doing so. METHODS: A systematic review of the use of apheresis for UC treatment was performed in June 2004 and updated in May 2008. Two related clinical questions were selected, the outcomes of interest defined, and the quality of the evidence assessed. Finally, the overall quality of each question was taken into account to formulate recommendations following the GRADE approach. To evaluate this experience, a SWOT (strengths, weaknesses, opportunities and threats) analysis was performed to enable a comparison with our previous experience with the SIGN (Scottish Intercollegiate Guidelines Network) method. RESULTS: Application of the GRADE approach allowed recommendations to be formulated and the method to be clarified and made more explicit and transparent. Two weak recommendations were proposed to answer to the formulated questions. Some challenges, such as the limited number of studies found for the new technology and the difficulties encountered when searching for the results for the selected outcomes, none of which are specific to GRADE, were identified. GRADE was considered to be a more time-consuming method, although it has the advantage of taking into account patient values when defining and grading the relevant outcomes, thereby avoiding any influence from literature precedents, which could be considered to be a strength of this method. CONCLUSIONS: The GRADE approach could be appropriate for making the recommendation development process for Health Technology Assessment (HTA) reports more explicit, especially with regard to new technologies.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.392
Teacher spread0.361 · 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