068 The effective management of idiopathic intracranial hypertension delivered by in-person and virtual group consultations
Bibliographic record
Abstract
Background In response to the rising incidence of IIH and service pressures, we set out to develop a group consultation(GC) service for IIH. Method Iterative, co-designing(with patients) a bespoke GC, i.e. clinical reviews in a group setting. Outcomes measured: patient satisfaction, self-perceived health literacy, successful implementation of GC. Results Eight in-person GCs delivered: once-monthly(Oct-Dec 2019), then twice-monthly(Jan-Feb 2020). Feedback from 49/53: 100% felt more satisfied and heard; 100% felt more involved in decision making; 98% had a better understanding of their condition; 96% felt more able to cope with their condition and keep themselves healthy; 94% rated this as a positive experience; 90% reported improved access and more time with their clinician compared to existing 1:1 appointments. Since Sept 2020 (in response to COVID-19 pandemic) we have delivered once-weekly virtual GC (18 to date). Feedback median scores: patient satisfaction 9.5/10; being listened to by clinician 10/10; involved by clinician in treatment decisions 10/10; clinician explanation of treatment 10/10; opportunity to discuss condition or treatment 10/10). Conclusions GC is safe and effective for IIH and preferred in our cohort. This allowed ongoing high-quality, person-centred care despite the COVID19 pandemic. We will also share the potential for GC for other neurological conditions. drsuiwong@gmail.com 59
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".