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 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 | 0.087 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".