P65 Implementing a pain scale to improve botulinum toxin practice for cervical dystonia
Bibliographic record
Abstract
Objectives To evaluate the effectiveness of introducing a pain scale to improve cervical dystonia (CD) patient satisfaction rates in the National Hospital for Neurology and Neurosurgery (NHNN) Botox clinic. Design Case control study. Subjects Subjects included CD patients attending the NHNN Botox clinic to receive injections. Methods Injectors were educated about the Toronto Western Spasmodic Torticollis Rating pain subscale (TWSTRS) and subsequently incorporated it into their standard assessment of CD patients prior to injections. Surveys were created and disseminated to patients immediately following their appointment to assess their opinions of the clinical team. Information was entered into Microsoft Excel and analysed using appropriate statistical methods. Results were compared with a previous NHNN Botox clinic audit. Results 42 surveys were collected in total from CD patients over a 4 week period. 36 patients (85.7%) reported pain associated with the condition. In comparison to an audit conducted in 2016, involving a similar sample size (n=40, with 28 reporting pain), a higher proportion of CD patients felt their pain was well understood by the clinical team (89.3% vs 94.4%). Furthermore, a higher proportion felt the team were competent in managing their pain (67.9% vs 94.4%). Conclusions Our study supports the use of a TWSTRS pain subscale to improve CD patient satisfaction rates in the Botox clinic. Further studies are encouraged to validate these findings and determine other suitable pain scales for implementation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".