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Record W4415438811 · doi:10.1302/1358-992x.2025.10.123

EQUITY OF ACCESS TO ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION AND ROTATOR CUFF REPAIR FOR INDIGENOUS PEOPLES: A PROVINCIAL COHORT STUDY

2025· article· en· W4415438811 on OpenAlexaffabout
M. Fowler-Woods, Sheila McRae, P.B. MacDonald, Heather J. Prior, Marc Morissette, Jarret M. Woodmass

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousOrthopedic surgeryAnterior cruciate ligament reconstructionRotator cuffOddsCohortRetrospective cohort studyCohort study

Abstract

fetched live from OpenAlex

For decades there have been repeated calls to address health inequities experienced by Canada's Indigenous peoples (First Nations, Inuit, and Metis). In 2015, the Truth and Reconciliation Committee (TRC) stated that closing the health gap between Indigenous and non-Indigenous people is critical to the reconciliation process. The purpose of this study was to evaluate access to care with respect to two commonly performed orthopaedic surgeries, anterior cruciate ligament reconstruction (ACLR) and rotator cuff repair (RCR). Approvals were obtained from the First Nations Health & Social Secretariat of Manitoba (FNHSSM), Manitoba Inuit Association (MIA), and Manitoba Metis Federation (MMF) to undertake this study. This was a retrospective population-based cohort study comparing Indigenous and non-Indigenous peoples with respect to operative (ACLR, RCR) versus non-operative management (2015–2020). For those that underwent surgery, time from diagnosis by a primary care physician to orthopedic surgery consult (time to consult) and from consult to surgery (time to surgery) (1990–2020) were compared between groups. Data collated through the Manitoba Centre for Health Policy was utilized. For each procedure, logistic regression was performed using surgery as the dependent variable and Indigenous status as the independent variable. Kaplan-Meier survival curves were generated to compare time to consult and time to surgery for Indigenous versus non-Indigenous patients. Between 2015–2020, 91 Indigenous patients and 1363 non-Indigenous patients underwent ACLR, while 126 and 789 patients with the same diagnostic codes, respectively, did not. The odds of non-Indigenous patients undergoing surgery is 2.39 times (95% CI 0.79) that of Indigenous patients (p 0.001) but not for time to surgery (p=0.50). 299 Indigenous and 6496 non-Indigenous patients underwent RCR in the same time frame. Mean time to consult was 1.16 (95% CI 0.29) years and 1.04 (95% CI 0.01) years, for Indigenous compared to non-Indigenous patients, respectively. Mean time to surgery was 0.58 (95% CI 0.06) and 0.54 (95% CI 0.01) years, respectively. Survival curves were different for time to consult (p>0.02) but not for time to surgery (p=0.08). Indigenous patients diagnosed with ACL and RC tears experience longer wait times to consult a surgeon and are less likely to undergo surgical reconstruction for these injuries than non-Indigenous patients. This study is consistent with other reports that show access to care for Indigenous patients is a pervasive issue throughout the Canadian health care system. Such evidence can be utilized to formulate a plan that aligns with the health-care related TRC calls to action.

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)0.004
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 score0.195
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.341
Teacher spread0.318 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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