Inter-rater reliability in performance status assessment among health care professionals: a systematic review
Bibliographic record
Abstract
BACKGROUND: Studies have reported that performance status (PS) is a good prognostic indicator in patients with advanced cancer. However, different health care professionals (HCPs) could grade PS differently. The purpose of this review is to investigate the PS scores evaluated by different HCPs as reported in the literature. METHODS: A literature search was conducted in Ovid MEDLINE and OLDMEDLINE from 1946 to Present (July 5, 2015), Embase Classic and Embase from 1947 to 2015 Week 26, and Cochrane Central Register of Controlled Trials up to May 2015. Information of interest was whether there was a difference of PS assessment between HCPs. Other statistical information provided to assess the agreement in ratings, such as Cohen's kappa coefficient, Krippendorff's alpha coefficient, Spearman Rank Coefficient, and Kendall's correlation, was noted. RESULTS: Of the fifteen articles, eleven compared PS assessments between HCPs of different disciplines, one between the attending and resident physician, two between similarly-specialized physicians, and one between two unspecified-specialty physicians. Three studies reported a lack of agreement (kappa =0.19-0.26; Krippendorff's alpha =0.61-0.63), four reported moderate inter-rater reliability (kappa =0.31-0.72), two reported mixed reliability, and six reported strong reliability (kappa =0.91-0.92; Spearman rank correlation =0.6-1.0; Kendall's correlation =0.75-0.82). Four studies reported that Karnofsky performance status (KPS) had better inter-rater reliability than both the Eastern Cooperative Oncology Group Performance Status (ECOG PS) and the palliative performance scale (PPS). CONCLUSIONS: The existing literature cites both good and bad inter-rater reliability of PS scores. It is difficult to conclude which HCPs' PS assessments are more accurate.
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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 | 0.132 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".