Use of Patient-Reported Outcomes in the Context of Different Levels of Data*
Bibliographic record
Abstract
Update This article was updated on January 25, 2012, because of a previous error. In the EQ-5D subsection of the Methods section on page 68, the sentence that had previously read “This, in addition to limited responsiveness for some conditions, has to be weighed against the inherent low response rate of the instrument.” now reads “This, in addition to limited responsiveness for some conditions, has to be weighed against the inherent low respondent burden of the instrument.” There is increasing interest in measuring patient-reported outcomes as part of routine medical practice, particularly in fields like total joint replacement surgery, where pain relief, satisfaction, function, and health-related quality of life, as perceived by the patient, are primary outcomes. We review some well-known outcome instruments, measurement issues, and early experiences with large-scale collection of patient-reported outcome measures in joint registries. The patient-reported outcome measures are reviewed in the context of multidimensional outcome assessment that includes the traditional clinical outcome parameters as well as disease-specific and general patient-reported outcome measures.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".