A qualitative interview study exploring patients’ views and experiences of treatment for hidradenitis suppurativa in the UK
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a long-term skin condition where evidence for management after first-line treatment fails is limited and practice varies across the UK. Medical and surgical treatment options are potential avenues of treatment. Furthermore, patient perspectives on HS treatments have received little attention in research to date. OBJECTIVES: To explore patients' views and experiences of treatment for HS to inform clinical care. METHODS: We conducted a nested qualitative study within a prospective cohort study. Interviews with 35 participants were completed by telephone. Purposive sampling was undertaken. Framework analysis was used to develop themes. RESULTS: Past experiences and knowledge informed patient beliefs and whether an individual felt a treatment option was appropriate or a good 'fit' for them at a specific moment in time. Healthcare professional recommendations can influence a patient's views and which treatment option they ultimately receive. Positive experiences were reported across all treatment types covered in the study. Negative experiences included mediation side-effects, lack of efficacy, delays to procedures and burden of wound care. However, even when personal experiences were not wholly positive for an individual, participants often believed the same treatment may potentially help others with HS, owing to the importance placed on personalization of treatment. CONCLUSIONS: This paper has implications for how healthcare professionals discuss treatment options with people with HS. A 'one-size-fits-all' approach is inappropriate, and shared decision-making that elicits patients' beliefs and preferences is crucial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".