Insights from using an outcomes measurement properties search filter and conducting citation searches to locate psychometric articles of tools used to measure context attributes
Bibliographic record
Abstract
Abstract Objectives To describe our experience with using a methodological outcomes measurement search filter (precise and sensitive versions of a filter designed to locate articles that report on psychometric properties of measurement tools) and citation searches to locate psychometric articles for tools that can be used to measure context attributes. To compare the precise filter when used alone and with reference list checking to citation searching according to number of records found, precision, and sensitivity. Results Using the precise filter, we located 130 of 150 (86.6%) psychometric articles related to 22 of 31 (71.0%) tools that potentially measured an attribute of context. In a subset of six tools, the precise filter alone was more precise than searching with the precise filter combined with reference list searching, or citation searching alone. The precise filter combined with reference list checking was the most sensitive search method examined. Overall, we found the precise filter helpful for our project as it decreased record screening time. For non-patient reported outcomes tools, we had less success with locating psychometric articles using the precise filter because some psychometric articles were not indexed in PubMed. More research that systematically evaluates database searching methods is needed to validate our findings.
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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 | 0.493 | 0.784 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.060 | 0.043 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".