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Record W2909478957 · doi:10.1186/s12913-019-3879-6

Are injured workers with higher rehabilitation service utilization less likely to be persistent opioid users? A cross-sectional study

2019· article· en· W2909478957 on OpenAlexaff
Alyson Kwok, Nathan N. O’Hara, Andrew N. Pollak, Lyndsay M. O’Hara, A Herman, Christopher Welsh, Gerard P. Slobogean

Bibliographic record

VenueBMC Health Services Research · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsMedicineRehabilitationMedical prescriptionOpioidCross-sectional studyPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Given its role in treating musculoskeletal conditions, rehabilitation medicine may be an important factor in decreasing the use of opioids among injured workers. The primary objective was to determine if increased utilization of rehabilitation services was associated with decreased persistent opioid use among workers' compensation claimants. The secondary objective was to determine the combined association of rehabilitation service utilization and persistent opioid use with days of work lost due to injury. METHODS: Using Chesapeake Employers' Insurance Company claims data from 2008 to 2016, claimants with at least one filled opioid prescription within 90 days of injury were eligible for inclusion. The primary outcome was persistent opioid use, defined as at least one filled opioid prescription more than 90 days from injury. The secondary outcome was days lost due to injury. The primary variable of interest, rehabilitation service utilization, was quantified based on the number of rehabilitation service claims and grouped into five levels (no utilization, and four quartiles - low, medium, high, very high). RESULTS: Of the 9596 claimants included, 29% were persistent opioid users. Compared to claimants that did not utilize rehabilitation services, patients with very high rehabilitation utilization were nearly three times more likely (OR: 2.71, 95% CI: 2.28-3.23, p < 0.001) to be persistent opioid users and claimants with low and medium levels of rehabilitation utilization were less likely to be persistent opioid users (low OR: 0.20, 95%: 0.14-0.27, p < 0.001) (medium OR: 0.26, 95% CI: 0.21-0.32, p < 0.001). Compared to claimants that did not utilize rehabilitation services, very high rehabilitation utilization was associated with a 27% increase in days lost due to the injury (95% CI: 21.9-32.3, p < 0.001), while low (- 16.4, 95% CI: -21.3 - -11.5, p < 0.001) and medium (- 11.5, 95% CI: -21.6 - -13.8, p < 0.001) levels of rehabilitation utilization were associated with a decrease in days lost due to injury, adjusting for persistent opioid use. CONCLUSION: Our analysis of insurance claims data revealed that low to moderate levels of rehabilitation was associated with reduced persistent opioid use and days lost to injury. Very high rehabilitation utilization was associated with increased persistent opioid use and increased time from work.

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)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.122
GPT teacher head0.446
Teacher spread0.324 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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