Trends in provision of photodynamic therapy and clinician attitudes: a tracker survey of a new health technology
Bibliographic record
Abstract
BACKGROUND: There has been debate about the cost-effectiveness of photodynamic therapy (PDT), a treatment for neovascular age-related macular degeneration. We have been monitoring trends for the provision of PDT in the UK National Health Service. The fourth annual 'tracker' survey took place as definitive National Institute for Clinical Excellence (NICE) guidance was issued. We assessed trends in PDT provision up to the point of release of the NICE guidance and identified likely sources of pressure on ophthalmologists to provide PDT. METHODS: National postal questionnaire survey of clinicians with potential responsibility for PDT provision. The survey explored reported local provision, beliefs about the effectiveness of PDT and what sources of opinion might influence attitudes towards providing PDT. RESULTS: The response rate was 73% (111/150). Almost half of the surveyed ophthalmology units routinely provided PDT, as part of a trend of steady growth in provision. The proportion of respondents who believed that further proof of effectiveness was required has also declined despite the absence of any new substantial evidence. Attitudes towards providing PDT were positive, on average, and were more strongly associated with perceived social pressure from local colleagues than from other sources. Local colleagues were seen as being most approving of PDT. CONCLUSION: Those responsible for implementing the NICE guidance need to address ophthalmologists' beliefs about the evidence of effectiveness for PDT and draw upon supportive local individuals or networks to enhance the credibility of the guidance.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".