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Record W4413274621 · doi:10.1093/jpepsy/jsaf062

Commentary: Wired for pain? Understanding brain connectivity and socioemotional factors in adolescents and young adult females following orthopedic surgery

2025· article· en· W4413274621 on OpenAlexaff
Emma E. Truffyn, C. Meghan McMurtry

Bibliographic record

VenueJournal of Pediatric Psychology · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcMaster UniversityWestern UniversityMcMaster Children's HospitalUniversity of Guelph
Fundersnot available
KeywordsSocioemotional selectivity theoryOrthopedic surgeryPsychologyMedicineDevelopmental psychologyNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

Sieberg and colleagues (this issue) deliver valuable insights into the underlying neural and psychosocial mechanisms of chronic post-surgical pain (CPSP) in female adolescents and young adults (AYAs). CPSP is persistent pain that extends beyond 2 months following surgery and has been increasingly recognized as a critical outcome of orthopedic procedures (Einhorn et al., 2024). In addition to known psychological risk factors (e.g., anxiety, depression, pain-related worry), central nervous system functions, including central sensitization and pain inhibitory processing, seem to play a crucial role in the development of CPSP (Chow et al., 2020; Sieberg et al., 2022). AYA females appear disproportionately impacted by CPSP, with higher incidence, greater pain sensitivity, functional disruptions, and in some cases, reduced effectiveness, as well as side effects from standard treatments (i.e., opioid medications) (Angst et al., 2012; Casale et al., 2021; Kanaan et al., 2021). Critically, there is a dearth of research on the interactions of biological, psychological, and social mechanisms contributing to the development, persistence, and treatment of CPSP in AYA females.

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)0.002
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 score0.063
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.054
GPT teacher head0.349
Teacher spread0.295 · 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
Published2025
Admission routes1
Has abstractyes

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