Bibliographic record
Abstract
The measurement of patient-reported outcomes, including health-related quality of life, is a new initiative which has emerged and grown over the past four decades. Following the development of reliable and valid self-report questionnaires, health-related quality of life has been assessed in tens of thousands of patients and a wide variety of cancers. This review is based on a selection of data published in the last decade and is intended primarily for healthcare professionals. The assessments in clinical trials have been particularly useful for elucidating the effects of various cancers and their treatments on patients' lives and have provided additional information that enhances the usual clinical endpoints used for determining the benefits and toxicity of treatment. With growing experience the quality of the health-related quality of studies has improved and, in general, recent studies are more likely to be methodologically robust than those that were performed in earlier decades. Health-related quality of life has become a more accurate predictor of survival than some other clinical parameters, such as performance status. The overall outlook for the routine assessment of patient-reported outcomes in clinical trials is assured and, eventually, it is likely to become a standard part of clinical practice. However, there is still a need for a clear method for determining the clinical meaningfulness of changes in scores. The answer will probably come from the greater use of patient-reported outcomes and the consequent growth of experience that is necessary to make such judgements.
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 | 0.196 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".