Inconsistencies in Nice Guidance for Acupuncture: Reanalysis and Discussion
Bibliographic record
Abstract
BACKGROUND: Acupuncture received a positive recommendation in the National Institute for Health and Clinical Excellence (NICE) clinical guideline for low back pain (LBP). However, no such recommendation was forthcoming in the NICE clinical guideline for osteoarthritis (OA). Importantly, the two guidelines adopted different treatment comparators in their economic analyses of acupuncture; in the LBP guideline 'usual care' was used (with no consideration of placebo/sham interventions), whereas 'sham acupuncture' was the comparator in the OA guideline. OBJECTIVE: To analyse the implications of using different control group comparators when estimating the cost-effectiveness of acupuncture therapy. METHODS: The NICE OA economic analysis for acupuncture was replicated using 'usual care' (ie, no placebo/sham component) as the treatment comparator. A 'transfer-to-utility' technique was used to transform Western Ontario and McMaster Osteoarthritis scores into EQ-5D utility scores to allow quality-adjusted life year (QALY) gains to be estimated. QALY estimates were combined with direct incremental cost estimates of acupuncture treatment to determine incremental cost-effectiveness ratios (ICERs). RESULTS: When 'usual care' was used as the treatment comparator, ICER point estimates were below £20 000 per QALY gained for each acupuncture trial analysed in the OA clinical guideline. In the original analysis, using placebo/sham acupuncture as the treatment comparator, ICERs were generally above £20 000 per QALY gained. CONCLUSION: The treatment comparator chosen in economic evaluations of acupuncture therapy is likely to be a strong determinant of the cost-effectiveness results. Different comparators used in the OA and LBP NICE guidelines may have led to the divergent recommendations in the guidelines.
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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 | 0.341 | 0.747 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.038 | 0.084 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".