29th Annual Conference of the International Society for Quality of Life Research
Bibliographic record
Abstract
Aims: The minimal important change (MIC) is loosely defined as the smallest change in a patient-reported outcome measure (PROM) score that patients value as important. Anchor-based methods using an external criterion reflecting the patients' perspective are preferred to estimate MICs. A popular anchor is the 1-item transition rating (TR). A TR requires patients to compare their perceived change with internal thresholds for e.g. ''a little better'' and ''much better''. The target MIC (for improvement) to be estimated is the mean of the individual thresholds between TR categories ''the same'' and ''a little better''. This MIC value can be estimated using longitudinal IRT (LIRT; Bjorner et al., under review). The current study aims to demonstrate that longitudinal structural equation modeling (LSEM) can do same job. Methods: We used simulations to be able to control the true MIC value (i.e., the population value of the mean threshold between ''the same'' and ''a little better''). We used IRT to simulate baseline and follow-up datasets for a hypothetical 10-item PROM. We created a perceived change variable by adding (measurement) error to the latent change variable. Furthermore, a variable of thresholds between ''the same'' and ''a little better'' (with a mean of 0.5 theta change) was created. So, the true MIC in the theta metric was 0.5. TR responses were simulated by comparing the perceived change with the thresholds. The simulated samples were varied with respect to the mean baseline score, the mean change score, and other characteristics. The LSEM-model used to estimate the MIC is shown in Fig. The MIC in terms of theta change is sTR/kTR2 where sTR represents the TR threshold between ''the same'' and ''a little better''. Results: Across 108 simulated samples the mean MIC (in terms of theta change) was 0.50 (95% CI 0.37, 0.61). For comparison the same MIC based on LIRT was 0.51 (95% CI 0.37, 0.62). The MIC was not affected by the proportion of improved patients, nor by any other sample characteristic. The LSEM estimations took considerably less time than the LIRT estimations. Conclusion: The true MIC can be accurately recovered using LSEM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.248 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".