REFLECTIONS ON THE NICE DECISION TO REJECT PATIENT PRODUCTION LOSSES
Bibliographic record
Abstract
OBJECTIVES: Patient production losses occur when individuals' capacities to work, whether paid or unpaid, are impaired by illness, treatment, disability, or death. There is controversy about whether and how to include patient production losses in economic evaluations in health care. Patient production losses have not previously been considered when evaluating medications for reimbursement under the U.K. National Health Service. Proposals for value-based assessment of health technologies in the United Kingdom created renewed interest in whether and how to include costs from a wider societal perspective, such as patient production losses, within economic evaluation of healthcare interventions. METHODS: A narrative review was undertaken of theoretical, ethical, and policy issues that might inform decisions that involve the normative question of whether or not to include patient production losses in economic evaluation. RESULTS: It seems difficult to reconcile the implications of including patient production losses with the objectives of a healthcare system dedicated to providing universal healthcare coverage without regard to patients' ability to pay. CONCLUSIONS: Tax payer funded healthcare systems may legitimately adopt maximands other than health gain, but these will be at the opportunity cost of less than maximum health gains.
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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 | 0.228 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.102 | 0.098 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".