Quality-of-Life Measurement in Surgical Randomized Controlled Trials
Bibliographic record
Abstract
Quality-of-life measurement in controlled clinical trials assessing medical treatment has increased drammatically over the past decades. Although the experience with quality-of-life measurement in surgical clinical trials has been more recent, it has demonstrated the important role of these measures in determining the best treatment options as well as in clinical decisions. Two types of instruments are available to measure quality of life: generic instruments, and specific instruments. Both follow a rigorous scientific methodology that includes both a development and a validation phase. In the validation phase, instruments are assessed for their reproducibility, responsiveness, and validity. Ad hoc instruments, on the other hand, follow no such methodology and results can be open to interpretation. This review demonstrates that quality-of-life measurement in surgical clinical trials is both possible and clinically important. More study investigators will consider measuring quality of life using well-validated instruments when designing future surgical randomized controlled trials.
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 | 0.242 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".