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Record W4408662197 · doi:10.1016/j.jor.2025.03.016

Patients prefer In-Office Needle Arthroscopy (IONA) over traditional surgical arthroscopy

2025· article· en· W4408662197 on OpenAlexaffabout
Gurjovan Sahi, Ajay Shah, Aazad Abbas, Johnathan R. Lex, Jihad Abouali, Jay Toor

Bibliographic record

VenueJournal of Orthopaedics · 2025
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of ManitobaToronto East General HospitalCanada Research ChairsUniversity of Toronto
FundersArthrex
KeywordsMedicineArthroscopySurgeryGeneral surgery

Abstract

fetched live from OpenAlex

Purpose: In-Office Needle Arthroscopy (IONA) is an emerging technology that has been primarily studied as a diagnostic tool. Recent evidence shows that it is a cost-effective alternative to hospital- and community-based MRI with comparable accuracy. Although exciting for surgeons and administrators, little is known about patients' perceptions of IONA. Level IV evidence shows that patients with claustrophobia or contra-indications to sedation prefer IONA to MRI for diagnostic purposes. However, no study to date has examined patients' preferences regarding IONA and traditional surgical arthroscopy. Therefore, this study was conceived with the purpose of gathering patients' perspectives on IONA as an alternative to traditional surgical arthroscopy through semi-structured interviews. A secondary outcome was to determine the real-life financial impact with respect to profit and cost of introducing IONA at an academic mid-sized Canadian hospital. Method: All patients undergoing arthroscopic non-ligamentous knee surgery within a three-month period at a mid-sized academic hospital were approached for this study. A trained researcher conducted telephone interviews regarding patient experience with the entire surgical process, including diagnosis and treatment, suffering an injury, referral for MRI and sports surgeon, and booking arthroscopic surgery. Participants were provided information on IONA, including risks and benefits as an alternative to traditional arthroscopy, and were asked about their likelihood of choosing IONA as an alternative to their treatment pathway. Thematic and quantitative analysis was conducted based on interview results, with quantitative analysis conducted using a 5-point Likert scale. Financial analysis was conducted by observing the propensity to choose IONA via patients' response to the 5-point Likert scale and then modeled for cost effectiveness. Results: Twenty-one patients were interviewed. The mean age was 32.3 (SD: 9.8) years old with 12 (57.1 %) females. Mean time from surgery to interview was 10.2 weeks (SD: 11.4). In general, patients' perceptions of IONA were favorable. When asked how likely they would have been to opt for IONA over traditional arthroscopy, the mean response was "very likely" (4.10 [1.26]). The mean likelihood for males to select IONA was higher than females (4.78 versus 3.58). Common reasons for wanting IONA were to speed up the time between injury and surgery (n = 9), avoiding a general anesthetic/intubation and associated complications (n = 7), and avoiding the fear/anxiety of surgery (n = 6). Most patients listed the lack of primary data on the effectiveness, pain, revision rate, and PROMs as the primary hesitation (n = 6). Financial analysis revealed that IONA would reduce costs by $21,832.66 (p < 0.0001), resulting in an increase in profit of $21,468.80 (p < 0.0001). Conclusion: The most significant finding in this study is that IONA may be preferable by patients in a publicly funded healthcare system with limitations to operating room access. Patients believe that IONA can accelerate the diagnosis and treatment of meniscal injuries, allowing patients to avoid surgery via the traditional route of the operating room. Furthermore, it is shown to be a cost-effective alternative to MRI with similar diagnostic accuracy at a mid-sized Canadian institution.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.282
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.288
Teacher spread0.277 · 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 teacher head, 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

Citations3
Published2025
Admission routes2
Has abstractyes

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