MétaCan
Menu
← Back to cohort
Record W4411420080 · doi:10.1016/j.ard.2025.06.819

POS1475-HPR EXPLORING CHANGES IN BENEFIT STATUS IN THE YEAR BEFORE AND AFTER REHABILITATION: A CASE-CONTROL STUDY.

2025· article· en· W4411420080 on OpenAlexaff
M. Nilsen Skinnes, Rikke Helene Moe, Tonje Johansen, Hild Kristin Morvik, N. Farsund, Johanne Fossen, Rita Skårdal, H. Sørdal-Buen, Alperen Değirmenci, Andreas Habberstad, Joe Sexton, Ruby Del Risco Kollerud, Ingvild Kjeken, Ross Wilkie, Till Uhlig

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineRehabilitationPhysical therapyGerontology

Abstract

fetched live from OpenAlex

Background: Most industrialized countries experience a substantial increase in long-term sick leave and work disability benefits, and people leave the labour market permanently due to health problems or work disability. We have limited knowledge on how multidisciplinary rehabilitation impacts benefit status longitudinally across different diagnostic rehabilitation groups. Objectives: To explore the changes in benefit status over three years in a rehabilitation group compared to matched controls. Methods: This longitudinal multicentre cohort study (RehabNytte) involved 17 rehabilitation institutions from the Norwegian specialist health care service and more than 3700 patients, with the primary diagnosis (~38 %) being rheumatic and musculoskeletal diseases (RMDs). Participants received either multidisciplinary rehabilitation or usual care, with each rehabilitant (N = 2 710) propensity score matched to 37 760 non-rehabilitants (control group) from the Norwegian Labour and Welfare Administration, applying sociodemographic factors such as age, gender and region of residence, and benefit status for matching. Benefit status comprises days on sick leave, work assessment allowance and disability benefits, quantified by accumulating compensated whole workdays per person per month for the 3 year-period, with identical assessment points for the control group. Linear regression was used to explore differences between the two groups on days on sick leave, work assessment allowance and disability benefits in the year after rehabilitation, as well as subgroup analysis of the RMD population. Results: After propensity score matching, baseline age was approximately 41 (SD 12) years, and 70 % were females (Table 1). In the year before rehabilitation (months 1-12), mean (SD) benefit days per person per month for rehabilitation vs. control group were 2.3 (5.1) vs. 1.9 (4.8) for sick leave; 0.8 (3.7) vs. 0.7 (3.5) for work assessment allowance; and 0.5 (3.1) vs. 0.6 (2.8) for disability benefits. In the year post rehabilitation (months 25-36) the mean days on benefits per person per month were 0.2 days less (p>0.001) on sick leave in the rehabilitation group vs. the control group, 3.8 more days (p>0.001) on work assessment allowance in the rehabilitation group, and 0.5 more days (p>0.001) on disability benefits in the rehabilitation group (Figure 1). Subgroup analysis of people with RMDs in the year post rehabilitation showed that mean days on benefits per person per month for sick leave were not significantly different in the rehabilitation group vs. controls (p=0.9), but in the rehabilitation group, there were 1.5 more days (p>0.001) on work assessment allowance, and 1.7 less days (p>0.001) on disability benefits. Conclusion: In the year following rehabilitation, sick leave days decreased marginally, while there was an increase in work assessment allowance and disability benefits within the rehabilitation group, compared to controls. Notably, for people with RMDs, the rehabilitation group had significantly fewer days on disability benefits compared to the control group. The results suggest that rehabilitation may play a significant role in identifying those in need of in need of more permanent support while reducing sick leave. The marginal increase in disability benefits in the rehabilitation group, and the reduction in the RMD subgroup, suggest that rehabilitation may help prevent progression to long-term disability. Figure 1Mean days per person per month on benefits for the rehabilitation and control group for the tree year period. Vertical line is rehabilitation start (month 13). Solid line indicates rehabilitation group, dotted line indicates control group. Red line is sick leave, orange is work assessment allowance and blue is disability benefits. REFERENCES: [1] Hemmings, P., Prinz, C. (2020). SICKNESS AND DISABILITY SYSTEMS: COMPARING OUTCOMES AND POLICIES IN NORWAY WITH THOSE IN SWEDEN, THE NETHERLANDS AND SWITZERLAND . OECD,. Retrieved 14.11.23 from https://one.oecd.org/document/ECO/WKP(2020)9/en/pdf. [2] OECD. (2010). Sickness, Disability and Work: Breaking the Barriers . https://doi.org/10.1787/9789264088856-en. Table 1Distribution of sociodemographic factors and work disability benefits among participants receiving rehabilitation and a matched control group.Intervention groupControl groupN (%)N (%)Age (mean, SD)42.8 (11.7)41.1 (12.0)Gender (female)26 968 (72.1)28 266 (69.5)Region of residenceWest6409 (17.3)6440 (15.8)South east26 797 (71.6)29 391 (72.2)North734 (2.0)819 (2.0)Middle3476 (9.3)4035 (9.9)Diagnosis*Rheumatic and musculoskeletal diseases1130 (41.9)2126 (34.3)Cancer584 (21.6)-Mental health-1616 (26.1)Other main diagnosis986 (36.5)2461 (39.7)Mean days (SD) on benefits per month 1 year before rehab (month 1-12)Sick leave2.3 (3.5)1.9 (3.3)Work assessment allowance0.8 (3.2)0.7 (3.1)Disability benefits0.5 (3.1)0.6 (2.8)*Unmatched. SD: Standard Deviation Acknowledgements: NIL . Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.043
GPT teacher head0.355
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Rheumatic Diseases→Same topicTelemedicine and Telehealth Implementation→French-language works237,207→