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Record W3087227348 · doi:10.1097/corr.0000000000001485

CORR Insights®: Recurrent Instability and Surgery Are Common After Nonoperative Treatment of Posterior Glenohumeral Instability in NCAA Division I FBS Football Players

2020· letter· en· W3087227348 on OpenAlexaboutno aff
Blake M. Bodendorfer

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2020
Typeletter
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFootballOrthopedic surgerySports medicineInstabilityJoint instabilityAmerican footballSurgeryGeneral surgeryPhysical therapy

Abstract

fetched live from OpenAlex

Where Are We Now? Posterior shoulder instability tends to be the result of repetitive microtrauma [14]. Posterior shoulder instability only accounts for up to 10% of all instances of shoulder instability [10], but it may be more common in young military cadets [3, 10] and National Football League (NFL) combine participants than in the general population. Among NFL combine participants, MR images of 38% of players showed evidence of posterior labral tears [8]. Posterior shoulder instability and its associated pathologies are reported most frequently in weightlifters, football linemen, rugby players, swimmers, gymnasts, wrestlers, overhead athletes, and active-duty military service members. Patients with posterior shoulder instability may be treated with or without surgery. Nonoperative management is aimed at controlling pain and increasing stability, and these goals might be attained through a three-phase program: (1) static proprioceptive control through closed-chain kinetic movements with visual feedback; (2) dynamization through isokinetic balancing of the internal and external rotators to ultimately accomplish global concentric strengthening; and (3) dynamic, proprioceptive, open-chain, kinetic exercises for eventual return to sports [5]. There have been no comparative studies with different protocols to examine posterior shoulder instability, but this seems logical and comparable to the protocols examining anterior shoulder instability. After undergoing nonoperative care, as many as 70% of patients with posterior shoulder instability eventually undergo surgery [16]. Risk factors for undergoing surgery include BMI greater than 35 kg/m2 and contact or weightlifting athletes [16]. It has also been demonstrated that patients with evidence of a posterior labral tear on MRI who simultaneously had subjective complaints and objective examination results consistent with instability and those with increased glenoid retroversion and posterior humeral head subluxation were more likely to undergo surgery than patients without these factors [4]. For patients without substantial bone loss whose symptoms do not improve with nonoperative treatment, arthroscopic posterior capsulolabral repair is, in my experience, the most-commonly used approach, with operatively treated patients reporting high return to activity, low risk of recurrence [3], and clinically important improvements in patient-reported outcomes (for example, American Shoulder and Elbow Surgeons, Rowe, Walch-Duplay, Constant, and Single Assessment Numeric Evaluation scores) [6, 7]. Regardless, many athletes—particularly overhead athletes—do not return to previous levels of play [6], and some undergo revision surgery. Factors associated with these problems include the use of anchorless techniques, use of fewer than four anchors, being a woman, and surgery on the dominant shoulder [13]. Tennent et al. [15] reported a prospective observational case series of National Collegiate Athletic Association Division I Football Bowl Subdivision players from three United States Military Service Academies who sustained posterior shoulder instability and opted for initial nonoperative management. The authors found that although seven of 10 players were able to return to play in the same season (and commonly during the same game in which they were injured), recurrent instability was common, and all of the players who returned to play eventually opted for surgery. A previous study reported that players in the NFL combine who underwent surgery played more by their second season than did players treated nonoperatively [8], and the present study by Tennent et al. [15] complements these data by encouraging caution when discussing with collision athletes the prognosis of nonoperatively treated posterior shoulder instability. Based on these data, I would encourage early surgical management when possible to facilitate fewer recurrent instability events and potentially fewer associated conditions such as complex labral tears and bone loss. Where Do We Need To Go? The ultimate goal of care in any patient with posterior shoulder instability should be early and effective treatment that minimizes the patient’s time away from a desired activity, sport, or occupation. To accomplish this goal, we need to focus on patient-centered and condition-specific measures when conducting research on this uncommon condition. Interestingly, in all of the previously mentioned studies [3-8, 10, 13, 14, 16], the authors did not use contemporary shoulder instability-specific patient-reported outcomes, such as the Western Ontario Shoulder Instability Index, Oxford Shoulder Instability Score, or Melbourne Instability Shoulder Score [11]. These are all valid, reliable, and responsive measures of shoulder instability. A further investigation analyzing the impact of type of sport, age, gender, and concomitant injuries on patient-centered and condition-specific outcomes after the treatment of posterior shoulder instability is warranted. How Do We Get There? Fortunately, posterior shoulder instability is an increasing focus of research. Most studies to date have been case series or case-control studies and prospective series, and when patient-reported outcome measures were used, they were not specific to shoulder instability [3-8, 10, 15, 16]. Although these are reasonable study designs, given the rarity of posterior shoulder instability, the largest area for improvement is to focus on contemporary, patient-centered, and condition-specific measures such as the Western Ontario Shoulder Instability Index, Oxford Shoulder Instability Score, and Melbourne Instability Shoulder Score. The importance of using condition-specific, patient-reported outcome measures is relatively straightforward. General metrics of health-related quality of life such as the SF-12 fail to capture joint-specific disability, making them less ideal for the study of specific pathologic conditions [17]. Even shoulder-specific outcome measures, such as the Constant score and simple shoulder test, show low content validity and responsiveness for disability associated with shoulder instability [11, 12]. Previous work on the Patient-Reported Outcomes Measurement Information System upper-extremity form has determined it has a near-excellent correlation with the Western Ontario Shoulder Instability Index, but with ceiling effects in patients younger than 21 years [2]. The benefit of the patient-Reported Outcomes Measurement System questionnaire is that it generally takes less time to complete than other surveys because of computerized adaptive testing, reducing burden on patients and research staff. Using these condition-specific patient-reported outcomes will help to determine whether patients are hitting the mark more accurately and reliably than the orthopaedic community has in the past, whether patients and physicians choose operative or nonoperative treatment. These more-accurate and more-reliable outcome measures may inform physicians, therapists, trainers, and patients as to the best strategies for treatment in order to minimize time off work and play. Using these data, we can create diagnostic algorithms so that this information is accessible and decisions can be determined quickly to attain the best likely outcome. Ultimately, implementing contemporary methods such as machine-learning into orthopaedic decision-making for posterior shoulder instability may assist surgeons, as it has for other musculoskeletal issues [1, 9].

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 score0.018
Threshold uncertainty score0.061

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0180.002

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 head0.147
GPT teacher head0.427
Teacher spread0.280 · 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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