468. CALCULATING THE MINIMAL CLINICALLY IMPORTANT DIFFERENCE FOR THE FACT-E FROM ESOPHAGECTOMY TO RECOVERY USING DISTRIBUTION-BASED METHODS
Bibliographic record
Abstract
Abstract Background The Functional Assessment of Cancer Therapy-Esophageal (FACT-E) is a validated patient-reported outcome instrument that measures four subscales of general cancer health and one subscale specific to esophageal cancer. To make the instrument more clinically useful, therapeutic thresholds known as minimal clinically important differences (MCID) are needed. The purpose of this study was to calculate the MCID for the FACT-E and its subscales for patients undergoing an esophagectomy. Methods The study was based on a retrospective analysis of a registry maintained by a high volume North American tertiary care referral centre, which includes the FACT-E collected between August 2004 and September 2023. The FACT-E was administered at nine time points over the course of a patient’s care, ranging from the time of first presentation to 5 years post-esophagectomy. This study used two distribution-based approaches – the standard error (SE) and the standard deviation (SD) – to calculate the MCID for the FACT-E’s total score and its five subscales. Nonparametric bootstrapping was used to generate 95% confidence intervals for each MCID. Results There were 731 participants included in this study but not all of those completed each subscale at each time point. The MCID for physical well-being ranged from 2.2-3.1 using SE and 2.3-3.4 using SD, from 2.1-3.6 using SE and 2.2-3.6 using SD for social well-being, 1.2-1.7 using SE and from 1.4-2.0 using SD for emotional well-being, 2.1-2.4 using SE and 3.2-3.6 using SD for functional well-being, and from 3.2-4.3 using SE and from 4.3-6.4 using SD for the esophagus cancer subscale. The MCID for the total score ranged from 6.7-8.3 using SE and from 8.9-11.2 using SD. Conclusion The MCID thresholds for the FACT-E subscales vary depending on when they are collected and how they are measured. In general, smaller thresholds of change are needed in the social and emotional well-being subscales, while larger ones are needed for the esophagus cancer subscale. Overall, the MCID for the FACT-E total score ranged from 6 to 11. Clinicians should consider these MCID if using the FACT-E to assess changes in patients’ symptom severity before and after esophagectomy.
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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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".