Hard choices: Reflections from the tomb of the unknown patient
Bibliographic record
Abstract
Health Technology Assessment (HTA) has always sought to incorporate the evidence of all patients affected in the decision-making process. While health system budgets could increase to cover costs of new technologies, the relevant patients are those benefitting from access to the technology being appraised. More recently, with health system budgets effectively fixed, costs of new technologies are covered by displacing other, currently funded care. This reallocation means the patients affected by the decision include those whose healthcare is displaced. These patients are typically unidentified, however, and so HTA in this instance involves choosing between identified and unidentified patients. We argue that HTA should take account of identifiability bias in this decision-making, to avoid promoting inequitable and inefficient access to healthcare.
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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 | 0.056 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.072 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.029 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 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".