An updated approach to determine minimal clinically important differences in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
INTRODUCTION: Current medications for idiopathic pulmonary fibrosis (IPF) have not been shown to impact patient-reported outcome measures (PROMs), highlighting the need for accurate minimal clinically important difference (MCID) values. Recently published consensus standards for MCID studies support using anchor-based over distribution-based methods. The aim of this study was to estimate MCID values for worsening in IPF using only an anchor-based approach. METHODS: We conducted secondary analyses of three randomised controlled trials with different inclusion criteria and follow-up intervals. The health transition question in the 36-Item Short-Form Health Survey (SF-36) questionnaire was used as the anchor. We used receiver operating curves to assess responsiveness between the anchor and 10 variables (four physiological measures and six PROMs). We used an anchor-based method to determine the MCID values of variables that met the responsiveness criteria (area under the curve ≥0.70). RESULTS: 6-min walk distance (6MWD), the St George's Respiratory Questionnaire (SGRQ), physical component score (PCS) of SF-36 and University of California, San Diego, Shortness of Breath Questionnaire (UCSD SOBQ) met the responsiveness criteria. The MCID value for 6MWD was -75 m; the MCID value for SF-36 PCS was -7 points; the MCID value for SGRQ was 11 points; and the MCID value for the UCSD SOBQ was 11 points. CONCLUSIONS: The MCID estimates of 6MWD, SGRQ, SF-36 and UCSD SOBQ using only anchor-based methods were considerably higher compared to previously proposed values. A single MCID value may not be applicable across all classes of disease severity or durations of follow-up time.
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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 | 0.096 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".