Bibliographic record
Abstract
Editor—Davey Smith et al have identified some problems with evidence based decision making in health care.1 Nevertheless, when these are set against the deficiencies of much current (non-evidence based) decision making, evidence based decision making still compares favourably. Administrators, facing complex allocation choices within tight budgets, are inclined to focus on economic notions of efficiency and fair play. The rationale is: “If it's not too expensive and seems to help a disadvantaged group we might be prepared to pay for it.” When people are presented with a problem (often the solution is presented first, implying that there must be a problem) they gather whatever information will confirm the merit of the intended intervention as quickly as possible. Inequalities in health are not remedied, nor the health of the population as a whole benefited, by this short term damage control. Computed tomography is important in examining efficacy (the safety and benefits of treatments used under ideal conditions). But to be of value to policymakers, research should seek to identify evidence supporting effectiveness (whether an intervention is likely to do more good than harm in routine use). The evidence needed for sound policymaking should thus be much more comprehensive than attempts to extrapolate dubious principles from the findings of computed tomography. Evidence based decision making is, fundamentally, the process of ensuring that the right questions are asked. Is an intervention safe and effective (will it do more good than harm)? Who needs it? Can it be provided under conditions of equal accessibility? Who is the population at risk, and what are the relevant clinical and social determinants? What change may be expected in the burden of disease? What are the social consequences (what are the implications in power and dominance issues, and what public and private interests are being served)? If decisions are based on such comprehensive evidence then the budgetary issues that follow will be much more accurately circumscribed. Tools exist that can synthesise such data to scientific standards and provide logical and defensible conclusions about impacts on a system, a population, and society.2 Decisions can be then be made that are based on meaningful comparison with interventions competing for the same budget. Ultimately, the aim of decision making in health care should be to achieve not equal health standards (however low the ceiling) but good health for all population groups–or, to put it another way, the best care for the greatest number of people. SUE SHARPLES
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.485 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.059 | 0.056 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".