Health Economic Evaluation Methodological Quality Assessment Tools: A protocol for a systematic review
Bibliographic record
Abstract
eview question / Objective The aim of this systematic review is to find any existing methodological quality assessment checklists and tools for health economic evaluations. R a t i o n a l eT h e d e v e l o p m e n t o f a n e w methodological quality assessment tool assumes that there is no adequate existing tool related to the area being discussed.Therefore, before developing a new tool and assessment items, it is important to understand what already exists in the literature.In 2012, the Agency for Healthcare Research and Quality (AHRQ) conducted a systematic literature review of quality assessment tools to evaluate best practices for conducting health economic evaluations and identified ten economic evaluation quality assessment tools published between 1992 and 2011.The purpose of our systematic review is to identify quality assessment tools published after 2011. Condition being studied N/A. METHODSSearch strategy A systematic search strategy will be designed in collaboration with a University of Alberta Health Sciences librarian experienced in systematic reviews.To identify published academic literature, we will conduct database searches (Ovid MEDLINE, EMBASE,
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 | 0.239 | 0.320 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.081 | 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; 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".