ASPECTS OF PATIENT REPORTED OUTCOMES IN RARE DISEASES: A DISCUSSION PAPER
Bibliographic record
Abstract
OBJECTIVES: A patient reported outcome (PRO) is "any report of the status of a patient's health condition that comes directly from the patient without interpretation of the patient's response by a clinician or anyone else" (USFDA 2009). PROs are discussed widely, and many regard the patients' perspective on treatment benefit as very valuable. Although many PROs have shown satisfactory measurement properties including reliability, validity, and responsiveness, there is great concern about risk of bias, that is, in clinical trials. METHODS: Differences in perspectives of PRO measurement in rare diseases are given arising from methodology, clinical, HTA, and patient advocacy views. RESULTS: PROs are playing an important role in dealing with treatment benefit especially in small sample size as occurring often in rare diseases. Challenges remain especially regarding lack of responsiveness of generic measures, limited capture of all patient relevant aspects, study design and high risk of bias. CONCLUSIONS: PROs seem a valuable instrument to detect patient relevant aspects in rare diseases. They should be seen in addition to other approved assessment methods as randomized controlled trials but not as their substitute.
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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 | 0.120 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| 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".