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Record W4385412636 · doi:10.1177/2325967123s00283

Poster 313: More Severe Adverse Events are Associated with Worse Patient Reported Outcome Measures following Anterior Cruciate Ligament Reconstruction with and without Lateral Extra- articular Tenodesis Augmentation – Results from the Stability Randomized Trial

2023· article· en· W4385412636 on OpenAlexaff
Hana Marmura, Dianne Bryant, Robert Litchfield, Robert G. McCormack, Peter B. MacDonald, Tim Spalding, Peter Verdonk, Devin Peterson, Davide Bardana, Alex Rezansoff, Alan Getgood

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsFowler Kennedy Sport Medicine ClinicPan Am ClinicWestern University
Fundersnot available
KeywordsMedicineAdverse effectSurgeryPromAnterior cruciate ligamentRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Investigate how the severity of adverse events encountered during the follow up period of the Stability Study were associated with patient reported outcomes at two years post-operative. Methods: Stability is a pragmatic, multicenter, randomized clinical trial comparing single-bundle hamstring tendon ACLR with combined ACLR + LET. Patients aged 14-25 years with an ACL deficient knee were included (n = 618). Participants completed PROMs (KOOS, IKDC, ACL-QOL), and adverse events were documented pre-operatively and at 3, 6, 12 and 24 months postoperatively. Adverse events were categorized into four groups: none (no adverse event), minor medical (resolved spontaneously or with minimum medical management), minor surgical (event such as meniscus tear or stiffness that required surgical intervention but is not a graft rupture), contralateral ACL rupture, and graft rupture. A generalized linear model was used to compare mean PROM scores with the different levels of adverse events. Results: The rate of minor medical adverse events (11.2%), minor surgical adverse events (7.4%), and ipsilateral (7%) or contralateral (3%) ACL tears at 24 months post-operative were low considering the high-risk patient profile. There was no difference in the proportion of minor medical events, minor surgical events, or contralateral ACL ruptures between the ACLR only and ACLR + LET groups (p>0.05). The ACLR only group had a significantly higher rate of graft rupture (11 vs 4%, p<0.01). Increasing severity of adverse events were associated with lower PROM scores at 24 months post-operative. Patients who experienced any adverse event within the two-year follow up had significantly lower outcome scores than those with no events. When only adverse events in the first year post-operative were included, the influence of minor medical and minor surgical events was largely washed out suggesting the effect on outcomes may be due to recency rather than severity. However, graft tears and contralateral ACL tears within the first year led to significantly lower scores at two-years post-operative in all outcomes (p<0.01). Conclusions: Increasing severity of adverse events were associated with lower patient reported outcome measures at 2 years post-operative. Recent minor medical and minor surgical events worsened PROM scores, but this effect was largely eliminated within a year of the event. Patients who experience a graft rupture or contralateral ACL tear appear to have a significantly lower PROMs at 2 years post- operative, regardless of the time at which the tear occurs. Therefore, the benefit of the LET procedure reducing graft rupture outweighs the potential for less severe events such as hardware irritation/removal which will likely be less detrimental to patient-important outcomes.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 designRandomized trial
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
Published2023
Admission routes1
Has abstractyes

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