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Record W4214693600 · doi:10.23889/ijpds.v7i2.1736

Improving the diagnosis of cancer in primary care: a feasibility economic analysis of the ThinkCancer! study.

2022· article· en· W4214693600 on OpenAlexaboutno aff
Bethany Anthony, Stefanie Disbeschl, Nia Goulden, Annie Hendry, Julia Hiscock, Zoë Hoare, Ruth Lewis, Jessica Roberts, Jan Rose, Nefyn Williams, Dan B. Walker, Richard D Neal, Clare Wilkinson, Rhiannon Tudor Edwards

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)MedicineWelshAttendanceHealth careGovernment (linguistics)CancerActivity-based costingFamily medicineNursingMedical emergencyBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

BackgroundCancer survival in the UK remains low compared to other Western countries including Australia, Canada and European countries. Delays in cancer diagnosis have an adverse impact on patient well-being and survival. Welsh Government outline a number of challenges with respect to earlier cancer diagnosis, including a lack of awareness of ‘red flag’ symptoms and difficulties among GPs and other healthcare professionals in identifying cancers that present with vague or non-specific symptoms. For some cancers, earlier diagnosis is associated with greater survival, better patient experience and quality of life, and lower healthcare costs. MethodsThe ThinkCancer! intervention is a complex behaviour change intervention, which aims to change the behaviours of primary care practice teams when thinking of and acting on clinical symptoms that could be cancer. From an NHS perspective, we conducted a feasibility economic analysis of the ThinkCancer! intervention. We used micro-costing methodology to determine whether it was feasible to gather sufficient economic data to cost the ThinkCancer! intervention. Due to the Covid-19 pandemic, the intervention was mostly delivered remotely in a digital format. Intervention deliverers completed data collection sheets (including forms recording materials used and intervention deliverer time) and provided information on primary care staff attendance at each of the ThinkCancer! workshops. Budget impact analysis and sensitivity analysis were conducted to explore the costs of face-to-face delivery of the ThinkCancer! intervention as intended pre-COVID-19. FindingsThe total costs of delivering the ThinkCancer! intervention across 19 general practices in Wales was £25,030. Costs per practice ranged from £431 to £2,498, with an average cost per practice of £1,311 (SD: 579.5). The potential budget impact if the intervention were to be delivered face-to-face across the 19 general practices would be £34,630. Sensitivity analysis revealed that if the intervention were to be delivered by one GP educator, the total estimated cost for face-to-face delivery would be £31,232. With the addition of one support role assisting the GP educator with the intervention delivery, the total cost of face-to-face delivery is estimated to be approximately £33,138. ConclusionsResults of this feasibility study are being used to inform a definitive economic evaluation alongside a pragmatic randomised controlled trial. Primary care interventions to expedite the diagnosis of symptomatic cancer have the potential to reduce large costs to the NHS and improve patient and carer outcomes as later stage cancer treatments are often longer, more aggressive to patients, with larger associated healthcare costs compared to earlier stage treatment. http://wicked.bangor.ac.uk/

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)0.041
metaresearch head score (Gemma)0.073
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.073
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.127
GPT teacher head0.480
Teacher spread0.353 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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