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Record W2740463955 · doi:10.1177/0363546518778252

Forearm Flexor Injuries Among Major League Baseball Players: Epidemiology, Performance, and Associated Injuries

2018· article· en· W2740463955 on OpenAlexaff
Justin L. Hodgins, David P. Trofa, Steve Donohue, Mark Littlefield, Michael Schuk, Christopher S. Ahmad

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsForearmLeagueElbowMedicinePhysical therapyFlexor musclesEpidemiologyUpper limbPhysical medicine and rehabilitationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite evidence highlighting the importance of the forearm flexor muscles of elite baseball players, no studies have reported on the epidemiology of flexor strains and their associated outcomes. PURPOSE: To examine the incidence, associated injuries, and outcomes associated with forearm flexor injuries among major and minor league baseball players. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Injury data attributed to forearm flexor injuries among Major League Baseball (MLB) and minor league teams between 2010 and 2014 were obtained from the professional baseball Health and Injury Tracking System. This analysis included the number of players injured, seasonal timing of injury, days spent on the disabled list (DL), preinjury performance data, and subsequent injuries. RESULTS: A total of 134 and 629 forearm flexor injuries occurred in MLB and the minor leagues, respectively. The mean player age was 28.6 and 22.8 years in the MLB and minor leagues, respectively. The mean time spent on the DL for MLB players was 117.0 days, as opposed to 93.9 days in the minor leagues ( P = .272). Interestingly, pitcher performance declined in all categories examined leading up to the season of injury, with significant differences in walks plus hits per inning pitched ( P = .04) and strike percentage ( P = .036). Of MLB players with a forearm injury, subsequent injuries included 50 (37.3%) shoulder, 48 (35.8%) elbow, and 24 (17.9%) forearm injuries. Among injured minor league players, subsequent injuries included 170 (27.0%) shoulder, 156 (24.8%) elbow, and 83 (13.2%) forearm injuries. These rates of subsequent injuries were significantly higher compared with the rates of injuries sustained among players without forearm injuries in both leagues ( P < .001). Finally, 26 (19.4%) MLB and 56 (8.9%) minor league players required an ulnar collateral ligament reconstruction, rates that were significantly higher compared with players without a flexor strain ( P < .001). CONCLUSION: Flexor-pronator injuries are responsible for considerable time spent on the DL for elite players in MLB and the minor leagues. The most significant findings of this investigation illustrate that a flexor strain may be a significant risk factor for subsequent upper extremity injuries, including an ulnar collateral ligament tear.

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)0.002
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.321
Teacher spread0.297 · 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

Citations38
Published2018
Admission routes1
Has abstractyes

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