Calculating the minimally important difference for the FACT-E from esophageal cancer surgery to recovery using distribution-based methods
Bibliographic record
Abstract
A challenge with patient-reported outcomes is interpreting changes in scores. The minimally important difference (MID) represents the smallest meaningful change in a score. This study's objective is to calculate the MID for the Functional Assessment of Cancer Therapy-Esophageal (FACT-E) and examine whether the MID changes over time from treatment through recovery. This retrospective longitudinal study analyzed data from the McGill University Esophageal and Gastric Data- and Bio-Bank. Participants were adults who underwent esophageal cancer surgery and completed the FACT-E pre-surgery and at least once post-surgery. MIDs were calculated using two distribution-based approaches: standard deviation and standard error of measurement. MIDs were calculated for the five FACT-E domains and total score at multiple time points. The study included 676 participants. MIDs varied by domain and calculation method. The MIDs ranged from 1 to 3 points for most domains, 2 to 5 points for the esophagus cancer subscale, and 4 to 9 points for the FACT-E total score. The MIDs changed over time, with the greatest fluctuations found in the esophagus cancer subscale. This study provides the first estimates of MIDs for the FACT-E, offering clinicians and researchers guidance for interpreting meaningful changes in scores. The range of MIDs can help identify potentially important changes in patient-reported symptoms and quality of life over time. Further studies using additional methods to calculate MIDs are warranted to refine these estimates.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".