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Record W4280609926 · doi:10.1177/2325967121s00383

Biomarkers of Knee Joint Healing in Adolescents Following Anterior Cruciate Ligament Reconstruction: A Systematic Review

2022· review· en· W4280609926 on OpenAlexaff
Lisa E. Ek Orloff, Michael J. Del Bel, Nicholas J. Romanchuk, Sasha Carsen, Pascal Imbeault, Daniel L. Benoit

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsMedicineAnterior cruciate ligamentOsteoarthritisRehabilitationAnterior cruciate ligament reconstructionBiomarkerACL injurySystematic reviewPhysical therapyKnee JointMEDLINESurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Background: Anterior cruciate ligament (ACL) injuries are increasing in prevalence by 2.3% annually in adolescents and have been linked to an increased risk for early-onset knee osteoarthritis (OA). Current ACL rehabilitation guidelines do not directly account for the physiological healing process of the knee joint. Instead, the sole focus is on a patients’ perceived function and their ability to meet functional benchmarks in the lower extremities. The physiological healing process could provide prognostic insight for clinicians and physiotherapists when rehabilitating ACL-injured patients. Hypotheses/Purpose: The purpose of this systematic review was to identify existing literature to determine i) the most prevalent biomarkers for reflecting knee joint healing in patients after ACL reconstruction (ACLR), and ii) the quantity of these studies which include adolescents. Methods: Following PRISMA guidelines, Medline, Embase, SCOPUS and Web of Science databases were searched up until September 2020. Studies were included if they (1) included participants who had sustained a primary ACL injury and undergone a subsequent ACLR, and (2) measured at least one biomarker of knee joint healing at more than one time point. An NIH quality assessment tool was used to assess study quality. The following data were extracted: participant age; biological sample(s); biomarker(s) analyzed. Results: Seven studies met inclusion criteria for this systematic review (Table 1). Interleukin-6 (IL-6) and C-terminal crosslinking telopeptide of type II collagen (CTX-II) were the most prevalent biomarkers used in the literature (3/7 studies). These biomarkers reflected knee joint healing through consistent elevation and/or gradual decrease following ACLR. Six studies evaluated adult populations (age range; Table 1). One study (average age 19.6±4.5) evaluated the effect of age on biomarker levels of knee joint healing and showed a negative correlation between age and CTX-II concentrations (r=-.769, p < 0.001). Conclusions: This systematic review identified (i) seven studies which evaluated healing using biomarkers following ACLR, (ii) IL-6 and CTX-II were most commonly used, and (iii) there is little research evaluating the physiological healing of the knee joint in any cohorts, in particular adolescent patients following ACLR. Adolescents are unique from adults due to growth and sex hormone variation. Therefore, what little biomarker research completed in adults cannot simply be extrapolated to adolescents. In addition to existing biomechanical rehabilitation tasks, we propose that rehabilitation and return to activity assessment following ACLR in adolescents could be informed by the healing status of the knee, improving outcomes and reducing their risk of early-onset knee OA. [Table: see text]

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)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.320
Teacher spread0.291 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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