Development of the Functional Assessment of Cancer Therapy-Carcinoid Syndrome Symptom Index
Bibliographic record
Abstract
OBJECTIVE: To develop a symptom-focused index to evaluate representative symptoms, treatment side effects, and emotional and functional well-being of patients with carcinoid syndrome (CS). METHODS: The development of the Functional Assessment of Cancer Therapy-Carcinoid Syndrome Symptom Index (FACT-CSI) followed US Food and Drug Administration guidelines for the development of patient-reported outcome (PRO) measures and involved the following: (a) literature review; (b) interviews with 14 CS patients; (c) interviews with 9 clinicians; and (d) instrument development involving input from a range of PRO measure development and CS experts. The resulting draft instrument underwent cognitive interviews with 7 CS patients. RESULTS: Forty-six CS sources were reviewed. Analysis of patient interviews produced 23 patient-reported symptoms. The most frequently endorsed physical symptoms were flushing, diarrhea, abdominal pain, fatigue, and food sensitivity/triggers. Seven priority CS emotional and functional themes were also identified by patients. Expert interviews revealed 12 unique priority symptoms - the most common being diarrhea, flushing, wheezing, edema, abdominal pain/cramping, fatigue, and 8 emotional and functional concerns. Through an iterative process of team and clinical collaborator meetings, data review, item reduction and measure revision, 24 items were selected for the draft symptom index representing symptoms, emotional concerns, global assessment of treatment side effects, and functional well-being. Cognitive interview results demonstrated strong content validity, including positive endorsement of item clarity (>86% across items), symptom relevance (>70% for most items), and overall measure content (86%). CONCLUSIONS: The FACT-CSI is a content-relevant, symptom-focused index reflecting the highest priority and clinically relevant symptoms and concerns of people with CS.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".