Unacceptable Work State in Rheumatoid Arthritis: Establishment of Thresholds for Presenteeism and Clinical Measurement Instruments
Bibliographic record
Abstract
OBJECTIVE: We aimed to identify threshold values of presenteeism measurement instruments that reflect unacceptable work state in employed patients with rheumatoid arthritis (RA) and whether those thresholds can predict future adverse work outcomes (AWOs). Additionally, we assessed the performance of presenteeism thresholds previously established in axial spondyloarthritis (axSpA) among patients with RA for the same instruments. METHODS: Data from the multinational Patient-Reported Outcomes in Employment Study in Rheumatoid Arthritis (RA-PROSE) study were used. Thresholds to determine when patients consider themselves in an "unacceptable work state" were calculated at baseline for 4 instruments assessing presenteeism and for the patient global assessment of RA-related pain. Different approaches derived from the receiver-operating characteristic methodology were used. Accuracy of thresholds to predict AWO throughout 12 months was assessed and previously developed presenteeism thresholds for axSpA were also tested. RESULTS: A total of 104 employed patients were included: 15% of the patients considered themselves in an unacceptable work state, of which 7 (7%) had at least 1 AWO over 12 months. Thresholds of all instruments specifically developed in RA showed good performance vs the external criterion (area under the curve [AUC] > 0.75), except for the Quantity and Quality (QQ) method (AUC 0.62). The available axSpA thresholds were more accurate by reducing overestimation. The final optimal thresholds were Work Productivity and Activity Impairment Questionnaire (WPAI)-presenteeism ≥ 40, QQ method < 97, Workplace Activity Limitations Scale ≥ 0.75, 25-item Work Limitations Questionnaire with modified physical demands scale ≥ 29, and pain intensity ≥ 4. For AWO over 12 months, pain and WPAI performed best in predicting AWO. CONCLUSION: The final thresholds to assess unacceptable presenteeism for axSpA were also chosen as most accurate for use in RA. In addition, accurate thresholds of pain reflecting unacceptable work state are available.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".