Important questions for the malignant hematologist to consider when designing or evaluating a study with patient‐reported outcome measures (<scp>PROMs</scp>)
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs), which are measures of symptom burden, health-related quality of life (HRQoL), and therapy effectiveness have become increasingly important in clinical research. They are unique in that they are reported directly from the patient, without clinician interpretation, thereby avoiding clinician bias. With an increased focus on the patient at the center of health care, PROMs have been increasingly incorporated into clinical research, systematic reviews, and clinical guidelines. Despite the recognition of the importance of including PROMs into clinical haematologic cancer research, barriers have prevented their integration into cancer research. This review highlights the value of including PROMs into clinical haematologic cancer research and addresses the methodological challenges in using and evaluating PROMs. We propose important questions for the malignant haematologist to consider when designing or evaluating a study that includes PROMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.213 | 0.523 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".