METHODOLOGICAL CHALLENGES IN EVALUATING THE VALUE OF REGISTERS
Bibliographic record
Abstract
OBJECTIVES: Procedures and new medical devices are typically introduced into healthcare systems with limited evidence, when they might be ineffective or unsafe. Systematic data collection ("registers") can provide valuable "real world" evidence, but difficulties in funding registers are a major obstacle. A good economic case for the value of registers would therefore be useful. METHODS: (i) Literature search on specific purposes of registers. (ii) Surveys (a) of senior clinicians involved with registers, seeking examples of beneficial outcomes, and (b) of administrators, regarding costs of running registers. (iii) A scoping exercise for possible methods to value (financially) the outputs of registers. RESULTS: Four main categories of beneficial outcomes from registers were identified. These were-safety and quality assurance; training and quality improvement; complementing trial evidence and reducing uncertainty; and supporting trial research. Explicit examples of all these are presented, together with information about the costs of registers. Combining these with the scoping exercise we present suggestions for a methodology of assessing the value of registers across each of the categories. CONCLUSIONS: This study is unique in addressing methods for determining the financial value of registers, based on the amount they cost versus the financial benefits which may result from the evidence generated. Developing the suggested methods could support the case for funding new registers, by showing that their use can benefit healthcare systems through more efficient use of resources, so justifying their costs.
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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 | 0.782 | 0.926 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".