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Record W4395012004 · doi:10.1037/rep0000560

Factors associated with pain intensity and analgesic use during inpatient rehabilitation for hip fracture.

2024· article· en· W4395012004 on OpenAlexaff
Erin Y. Harmon, Li Shen Chong, Morgan D. Marruso

Bibliographic record

VenueRehabilitation Psychology · 2024
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsSunnybrook Hospital
Fundersnot available
KeywordsHip fractureAnalgesicMedicinePhysical therapyRehabilitationIntensity (physics)Physical medicine and rehabilitationAnesthesiaOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Effective pain management is vital for hip fracture recovery, yet the factors influencing pain reporting and pain medication use during inpatient rehabilitation for hip fractures are not well understood. This observational study aimed to (a) determine how cognitive abilities, expressive and receptive language abilities, and age are related to average daily pain intensity and analgesic use and (b) how average daily pain intensity and analgesic use are related to length of stay and functional outcomes in rehabilitation. DESIGN: Data were retrospectively obtained from 163 patients recovering from unilateral trochanteric fractures of the femur. RESULTS: = 0.03, 95% CI [-0.05, -0.01]). Average daily pain intensity and analgesic use were not related to functional outcomes in multivariable models. CONCLUSIONS: These findings inform the considerations for assessing and treating pain during inpatient rehabilitation. Supplemental strategies for assessing pain in older patients and alternative pain mitigation strategies for patients with impaired cognitive abilities should be considered. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.001
metaresearch head score (Gemma)0.005
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 score0.022
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.032
GPT teacher head0.328
Teacher spread0.296 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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