Using Generalizability Theory to Develop Clinical Assessment Protocols
Bibliographic record
Abstract
Clinical assessment protocols must produce data that are reliable, with a clinically attainable minimal detectable change (MDC). In a reliability study, generalizability theory has 2 advantages over classical test theory. These advantages provide information that allows assessment protocols to be adjusted to match individual patient profiles. First, generalizability theory allows the user to simultaneously consider multiple sources of measurement error variance (facets). Second, it allows the user to generalize the findings of the main study across the different study facets and to recalculate the reliability and MDC based on different combinations of facet conditions. In doing so, clinical assessment protocols can be chosen based on minimizing the number of measures that must be taken to achieve a realistic MDC, using repeated measures to minimize the MDC, or simply based on the combination that best allows the clinician to monitor an individual patient's progress over a specified period of time.
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 | 0.466 | 0.664 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".