MétaCan
Menu
Back to cohort
Record W2890178730 · doi:10.1007/s00167-018-5142-9

Tunnel osteolysis post-ACL reconstruction: a systematic review examining select diagnostic modalities, treatment options and rehabilitation protocols

2018· review· en· W2890178730 on OpenAlexaff
Ramandeep Singh Bhullar, Anthony Habib, Kailai Zhang, Darren de, Nolan S. Horner, Andrew Duong, Nicole Simunovic, João Espregueira‐Mendes, Olufemi R. Ayeni

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2018
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcMaster University Medical CentreImpactMcMaster University
Fundersnot available
KeywordsAnterior cruciate ligament reconstructionMedicineRehabilitationModality (human–computer interaction)Physical therapyMeta-analysisRadiographyWeight-bearingCochrane LibrarySurgeryAnterior cruciate ligamentRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this systematic review was to (1) identify the optimal diagnostic modality for tunnel widening in skeletally mature patients; (2) identify potentially modifiable risk factors for tunnel widening, such as graft type, and (3) determine what elements of a post-operative rehabilitation program exert the most influence on TW. METHODS: The electronic databases MEDLINE, EMBASE, PubMed, and Cochrane Library were searched from database inception to January 2018. Studies that discussed tunnel widening following anterior cruciate ligament reconstruction (ACLR) of skeletally mature patients and written in English were included. Descriptive statistics, such as means, ranges, and measures of variance (e.g. standard deviations, 95% confidence intervals (CI)) are presented where applicable. RESULTS: 103 studies (6,383 patients) were included. Plain radiographs were the most commonly used diagnostic modality, but radiographs on average required 10 months longer than CT and 2 months longer on average than MRI to diagnose tunnel widening after ACLR. Although CT was the least commonly used modality, it was the shortest time to diagnose tunnel widening at 9.5 months after ACLR. Bone-patellar tendon-bone (BPTB) allograft had the largest average tunnel widening overall. BPTB autograft had the lowest average tunnel widening overall. Double-bundle hamstring graft configuration had a lower average tunnel widening than single-bundle configuration. Rehabilitation protocols after ACLR that used a full weight-bearing prescription in rehabilitation showed a greater average femoral tunnel widening than partial weight-bearing, and partial weight-bearing showed a greater average tibial tunnel widening than full weight-bearing. CONCLUSIONS: Based on this systematic review and the descriptive data evaluated, CT demonstrated a time of 9.5 months on average from ACLR to diagnosing tunnel osteolysis post-ACLR. With respect to graft types, double-bundle hamstring autografts reported lower average femoral and tibial TW than single-bundle hamstring autografts. BPTB autografts reported the lowest average TW and BPTB allograft the largest average TW of all the grafts. Furthermore, extension-locked bracing had the lowest TW of all the brace protocols. Lastly, several other surgical technical parameters influencing tunnel osteolysis remain to be determined. No definitive recommendations can be made at this time due to the high heterogeneity of data and the lack of comparative studies analysed in this systematic review. LEVEL OF EVIDENCE: IV.

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)0.005
metaresearch head score (Gemma)0.032
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 score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.340
Teacher spread0.301 · 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".

Quick stats

Citations30
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueKnee Surgery Sports Traumatology ArthroscopySame topicKnee injuries and reconstruction techniquesFrench-language works237,207