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Record W2904634668 · doi:10.1177/2325967118813917

Relationship Between Time to ACL Reconstruction and Presence of Adverse Changes in the Knee at the Time of Reconstruction

2018· article· en· W2904634668 on OpenAlexaffabout
Mark Sommerfeldt, Tom Goodine, Abdul Raheem, Jackie L. Whittaker, David Otto

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAnterior cruciate ligament reconstructionQuartileAnterior cruciate ligamentTearsOsteoarthritisMeniscusSurgeryArticular cartilage damageMedical recordPhysical therapyInternal medicineArticular cartilageConfidence intervalIncidence (geometry)

Abstract

fetched live from OpenAlex

Background: Treatment of patients with anterior cruciate ligament (ACL) injuries is often complicated by secondary damage to the meniscus and cartilage. Purpose: To assess the association between time from ACL tear to ACL reconstruction (ACLR) and the presence of intra-articular injuries at the time of ACLR, including meniscal tears, irreparable meniscal tears, chondral damage, and knee compartment degenerative changes. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Consecutive patients undergoing primary ACLR performed by a single surgeon in a Canadian health system over a 5.5-year period were included. Age at ACLR, activity level prior to injury, time from injury to ACLR (TFI), presence and degree of radiographic osteoarthritic features (International Knee Documentation Committee [IKDC] score by tibiofemoral and/or patellofemoral compartment), and surgeon-recorded meniscal lesions (presence and treatment [repair or excision]) and chondral lesions (International Cartilage Repair Society [ICRS] scale grade >2) at time of ACLR were extracted from medical records. The association between TFI (in quartiles: first quartile [0-36 wk] through fourth quartile [110-1000 wk]) and each outcome was assessed with multivariable logistic regression adjusted for age at ACLR and activity level. Results: A total of 860 individual patient records were included. The median patient age was 27.0 years (range, 12-63 years), 47.5% were female (403/849), and 47.2% were classified as playing competitive or professional sports versus recreational sport (337/714). After adjustment for age and activity level, TFI was associated with presence of medial meniscal tear (odds ratio [OR] of fourth-quartile vs first-quartile patients, 3.86; 95% CI, 2.38-6.24; P < .001), medial meniscal tear requiring greater than two-thirds meniscectomy (OR, 5.64; 95% CI, 2.99-10.67; P < .001), medial femoral condyle chondral damage (OR, 3.42; 95% CI, 1.96-5.95; P < .001), and medial tibiofemoral radiographic osteoarthritic features (OR, 22.03; 95% CI, 5.17-93.86; P < .001). TFI was not associated with adverse outcomes in the lateral tibiofemoral or patellofemoral compartments. Conclusion: Increases in TFI are associated with medial meniscal tears, including irreparable medial meniscal tears, medial femoral condyle chondral damage, and early medial tibiofemoral compartment degenerative changes at time of ACLR. These findings highlight the importance of establishing a timely diagnosis and implementing an appropriate treatment plan for patients with ACL injuries. This approach may prevent further instability episodes that place patients at risk of sustaining additional intra-articular injuries in the affected knee. Further research is required to understand the implications of TFI and to determine whether decreasing the TFI alters the natural history after an ACL injury.

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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations42
Published2018
Admission routes2
Has abstractyes

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