Costing bias in economic evaluations
Bibliographic record
Abstract
Determining the cost-effectiveness of healthcare interventions is key to the decision-making process in healthcare. Cost comparisons are used to demonstrate the economic value of treatment options, to evaluate the impact on the insurer budget, and are often used as a key criterion in treatment comparison and comparative effectiveness; however, little guidance is available to researchers for establishing the costing of clinical events and resource utilization. Different costing methods exist, and the choice of underlying assumptions appears to have a significant impact on the results of the costing analysis. This editorial describes the importance of the choice of the costing technique and it's potential impact on the relative cost of treatment options. This editorial also calls for a more efficient approach to healthcare intervention costing in order to ensure the use of consistent costing in the decision-making process.
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 | 0.124 | 0.529 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".