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Record W4405043820 · doi:10.1177/10225536241302219

A qualitative investigation to identify return to sports criteria after shoulder stabilization surgery used by professional team physicians

2024· article· en· W4405043820 on OpenAlexaff
Mike Szlufcik, Mario Pasurka, John Theodoropoulos, Marcel Betsch

Bibliographic record

VenueJournal of orthopaedic surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineQualitative researchCoding (social sciences)Physical therapy

Abstract

fetched live from OpenAlex

Purpose: Purpose of this study is to explore currently utilized readiness to return to sports (RTS) criteria after shoulder stabilization surgery used in elite athletes to gain novel insights into the RTS decision making process of professional team physicians. Methods: 19 qualitative semi-structured interviews with professional team physicians were conducted by a single trained interviewer. The interviews were used to identify team physician concepts and themes regarding the criteria used to determine RTS after shoulder stabilization surgery. General inductive analysis and a coding process were used to identify themes and sub-themes arising from the data. A hierarchical approach in coding helped to link themes. Results: We were able to identify five key themes that participating physicians focused on to determine RTS decision making: external influence, objective and subjective criteria, time elapsed since surgery and type of sport. The most important RTS criteria included: range of motion and muscle strength followed by clinical joint stability, time since surgery, ability of sporting movement, psychological readiness, functional testing, absence of pain and allied team support. Conclusion: This study identified several main themes and subordinate minor themes as having the most influence on RTS decision after shoulder surgery. We showed that even among specialized professional team physicians, the main criteria to RTS in these categories were inconsistent necessitating the future development of specific RTS guidelines.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.407
Teacher spread0.352 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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