Thresholds for unacceptable work state in radiographic axial spondyloarthritis of four presenteeism and two clinical outcome measurement instruments
Bibliographic record
Abstract
OBJECTIVES: To (i) identify threshold values of presenteeism measurement instruments that reflect unacceptable work state in employed r-axSpA patients; (ii) determine whether those thresholds accurately predict future adverse work outcomes (AWO) (sick leave or short/long-term disability); (iii) evaluate the performance of traditional health-outcomes for r-axSpA; and (iv) explore whether thresholds are stable across contextual factors. METHODS: Data from the multinational AS-PROSE study was used. Thresholds to determine whether patients consider themselves in an 'unacceptable work state' were calculated at baseline for four instruments assessing presenteeism and two health outcomes specific for r-axSpA. Different approaches derived from the receiver operating characteristic methodology were used. Validity of the optimal thresholds was tested across contextual factors and for predicting future AWO over 12 months. RESULTS: Of 366 working patients, 15% reported an unacceptable work state; 6% experienced at least one AWO in 12 months. Optimal thresholds were: WPAI-presenteeism ≥40 (AUC 0.85), QQ-method <97 (0.76), WALS ≥0.75 (AUC 0.87), WLQ-25 ≥ 29 (AUC 0.85). BASDAI and BASFI performed similarly to the presenteeism instruments: ≥4.7 (AUC 0.82) and ≥3.5 (AUC 0.79), respectively. Thresholds for WALS and WLQ-25 were stable across contextual factors, while for all other instruments they overestimated unacceptable work state in lower educated persons. Proposed thresholds could also predict future AWO, although with lower performance, especially for QQ-method, BASDAI and BASFI. CONCLUSIONS: Thresholds of measurement instruments for presenteeism and health status to identify unacceptable work state have been established. These thresholds can help in daily clinical practice to provide work-related support to r-axSpA patients at risk for AWO.
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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 | 0.013 | 0.030 |
| 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 | 0.002 |
| 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".