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Record W3174039319 · doi:10.1055/s-0041-1730975

The Relationship between Kinesiophobia and Return to Sport after Shoulder Surgery for Recurrent Anterior Instability

2019· article· en· W3174039319 on OpenAlexaboutno aff
Alberto Vascellari, Carlo Ramponi, Davide Venturin, Giulia Ben, Nicolò Coletti

Bibliographic record

VenueJoints · 2019
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersUniversity of Queensland
KeywordsMedicinePhysical therapyReturn to sportSignificant differenceMinimal clinically important differenceSurgeryRehabilitationRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose To evaluate the relationship between kinesiophobia and patient's return to sport after shoulder stabilization surgery. The hypothesis was that kinesiophobia represents an independent factor correlated to the difference between preinjury and postoperative level of sport. Methods This study retrospectively evaluated 66 patients (mean age: 35.5, standard deviation [SD] = 9.9 years) and at a mean follow-up of 61.1 (SD = 37.5) months after arthroscopic Bankart's repair or open Bristow–Latarjet procedure. Kinesiophobia was assessed with the Tampa Scale for Kinesiophobia (TSK); return to the preinjury sport was assessed by the difference between baseline and postoperative degree of shoulder involvement in sport (D-DOSIS) scale. The Western Ontario Shoulder Instability index (WOSI) was used to evaluate participants' perceptions of shoulder function. Results TSK showed correlation with D-DOSIS (ρ = 0.505, p < 0.001) and the WOSI score (ρ = 0.589, p < 0.001). There was significant difference in TSK and WOSI scores between participants who had and had not returned to their previous level of sport participation (p = 0.006, and 0.0001, respectively). Conclusion This study demonstrated that kinesiophobia is correlated to the return to sport after shoulder stabilization surgery. Level of Evidence Level IV, retrospective case series.

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 score0.010
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

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

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 head0.060
GPT teacher head0.338
Teacher spread0.278 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

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