MétaCan
Menu
Back to cohort

P270 ‘Act on Axial SpA': a gold standard time to diagnosis

2022· article· en· W4224316252 on OpenAlexaff
Liz Marshall, Dale Webb, Fiona Macaulay, Karl Gaffney, Raj Sengupta

Bibliographic record

VenueLara D. Veeken · 2022
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsArthritis Society
Fundersnot available
KeywordsMedicineAxial spondyloarthritisPopulationDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Background/Aims A lack of public understanding of the symptoms of axial spondyloathritis (axial SpA) is a significant factor in diagnostic delay. Research commissioned by The National Axial Spondyloarthritis Society (NASS) found that 91% of the UK population had never heard of axial SpA, despite more people living with the condition than MS and Parkinson’s combined. Additionally, 8 in 10 people could not identify the symptoms of axial SpA when prompted. As part of a 5-year programme called ‘Act on Axial SpA’, NASS created the first phase of a campaign to increase public awareness, help people recognise symptoms, and encourage them to visit their GP if concerned. Aims: 1) Ensure people have heard of axial SpA. 2) Help people understand that it’s a condition that affects young people. 3) Ensure people recognise the signs and symptoms of the condition. 4) Get people to visit actonaxialspa.com to use the symptom checker and visit their GP if concerned. Methods 1) Raise awareness about the condition: to make an emotional connection with a cold audience, who are unlikely to have heard of axial SpA (AS) and show them why the campaign is relevant to them. We told stories about people with the condition and their families, so people can see that the condition could affect someone like ‘them’. We ensured our campaign key messages are highly visible in the places, publications and online media people in our target audiences are likely to see every day. 2) Identify a core set of symptoms using a newly developed acronym (SPINE). 3) Direct people to an online symptom checker which combines the ASAS, Berlin and Calin inflammatory back pain criteria. 4) Provide information for the patient and primary care professional on the results of the symptom checker and next steps as per the NICE guidelines 5) Provide information for the patient on preparing for their GP and rheumatologist appointments. Results We report results from June 23 - October 13 2021. 1) Case studies have featured in 11 national media publications with a combined reach of over 101 million. 2) Social media activity has a reach of 440,000. 3) Video content has been viewed 375,000 times. 4) 1,264 people have used the symptom checker. 5) We are reaching new audiences, in particular those aged 18-45 and a larger proportion of women. Conclusion The first phase of the campaign has demonstrated cut through to new audiences. People are interested, engaged and eager to learn more about axial SpA. When people are aware of the condition and its symptoms, they are more likely to act. We are confident that, with time, the ‘Act on Axial SpA’ public awareness campaign will play a huge part in reducing diagnosis times for people with axial SpA. Disclosure L. Marshall: None. D. Webb: None. F. MacAulay: None. K. Gaffney: Consultancies; Novartis, AbbVie, UCB, Lilly, Pfizer. Shareholder/stock ownership; SpA Academy www.spaacademy.org. Honoraria; Novartis, AbbVie, UCB, Lilly, Pfizer. Member of speakers’ bureau; Novartis, UCB, AbbVie, Lilly. Grants/research support; NASS, AbbVie, Pfizer, UCB, Novartis, Lilly, Cellgene, Celltrion, Janssen, Gilead, Biogen. Other; Expenses: Abbvie, Lilly, Roche, Novartis, Pfizer and UCB. R. Sengupta: Consultancies; Abbvie, Biogen, Celgene, Chugai, Lilly, MSD, Novartis, UCB. Honoraria; Abbvie, Biogen, Celgene, Chugai, Lilly, MSD, Novartis, UCB. Grants/research support; Abbvie, Celgene, Novartis, UCB. Other; Advisory boards:, Abbvie, Biogen, Chugai, Lilly, Novartis, UCB.

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)0.012
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.002
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.014

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLara D. VeekenSame topicSpondyloarthritis Studies and TreatmentsFrench-language works237,207