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Postoperative Pain After Surgical Treatment of Ankle Fractures: A Prospective Study

2018· article· en· W2909504300 on OpenAlexaboutno aff
Loretta B. Chou, Emily Niu, Ariel A. Williams, Rosanna Duester, Sophia E. Anderson, Alex H. S. Harris, Kenneth J. Hunt

Bibliographic record

VenueJAAOS Global Research and Reviews · 2018
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleMcGill Pain QuestionnaireProspective cohort studyPostoperative painSurgeryPhysical therapyVisual analogue scale

Abstract

fetched live from OpenAlex

BACKGROUND: Postoperative pain after fixation of ankle fractures has a substantial effect on surgical outcome and patient satisfaction. Patients requiring large amounts of narcotics are at higher risk of long-term use of pain medications. Few prospective studies investigate patient pain experience in the management of ankle fractures. METHODS: We prospectively evaluated the pain experience in 63 patients undergoing open reduction and internal fixation of ankle. The Short-Form McGill Pain Questionnaire was administered preoperatively and postoperatively (PP) at 3 days (3dPP) and 6 weeks (6wPP). Anticipated postoperative pain (APP) was recorded. RESULTS: No significant differences were found between PP, APP, and 3dPP; however, 6wPP was markedly lower. Significant correlations were found between PP and APP and between preoperative and postoperative Short-Form McGill Pain Questionnaire scores. PP and APP were independent predictors of 3dPP; however, only APP was predictive of 6wPP. Sex, age, and inpatient versus outpatient status were not notable factors. No statistically significant differences were found in pain scores between fracture types. CONCLUSIONS: Both preoperative pain severity and anticipated postoperative pain are predictive of postoperative pain levels. Orthopaedic surgeons should place a greater focus on the postoperative management of patient pain and expectations after surgical procedures.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.069
GPT teacher head0.441
Teacher spread0.372 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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