Inter-rater reliability in performance status assessment between clinicians and patients: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Performance status is an essential consideration for clinical practice and for patient eligibility for clinical trials in oncology. Assessment of performance status is traditionally done by clinicians, but there is an increasing interest in patient-completed assessment. The aim of this systematic review and meta-analysis was to summarise inter-rater concordance between patient and clinician ratings of performance status. METHODS: A search strategy was developed and executed in the databases of Ovid MEDLINE, Embase and Cochrane Central Register of Controlled Trials, from inception until 15 August 2019. Articles were eligible for inclusion if there was mention of both (1) use of performance status tool Karnofsky Performance Status (KPS) or Eastern Cooperative Oncology Group Performance Status (ECOG), and (2) assessment of performance status by both clinicians and patients. Pearson correlation coefficients were calculated for each study and were meta-analysed according to a random-effect analysis model. Analyses were conducted using Comprehensive Meta-Analysis (V.3) by Biostat. RESULTS: Sixteen articles were included in our review, reporting on a cumulative sample size of 6619 patients. The quality of evidence was moderate, as determined by the GRADE tool.Concordance ranged from fair to moderate for both the KPS and ECOG tools. The Pearson correlation coefficient was 0.449 for KPS and 0.584 for ECOG. CONCLUSIONS: There is fair to moderate concordance of patient and clinician performance status ratings. Future studies should examine the reasoning behind clinician and patient ratings to better understand discrepancies between ratings.
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 | 0.094 | 0.219 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".