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Exploring the Nexus: Socioeconomic Indices and Their Influence on Patient-Reported Outcomes in Oncologic Reconstructive Surgery at a Quaternary Care Center

2024· article· en· W4402582991 on OpenAlexaboutno aff
Danielle Rogan, Luis H. Camacho, Rami Elmorsi, David D. Krijgh, Gordon S. Tilney, Heather Lyu, Raymond Traweek, Russel G. Witt, Margaret S. Roubaud, Arlene M. Correa, Christen L. Roland, Alexander F. Mericli

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Socioeconomic statusCenter (category theory)MedicineQuaternaryReconstructive surgeryGerontologySurgeryEnvironmental healthEngineeringGeologyChemistry

Abstract

fetched live from OpenAlex

INTRODUCTION: The role of socio-environmental determinants in shaping postoperative outcomes, especially in the context of oncologic surgeries like lower extremity soft tissue sarcoma (STS) reconstructions, warrants deeper exploration given their noted impact across various surgical disciplines (1). This research aims to dissect the connection between socioeconomic factors, as gauged by the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI), and the outcomes reported by patients following their surgeries. METHODS: Our study was a single-institution, IRB-approved retrospective analysis of patients treated for lower extremity soft tissue sarcoma from 2016 to 2021. Data from 302 STS patients were analyzed for complications and patient-reported outcomes using PROMIS-29 (Patient-Reported Outcomes Measurement Information System) and TESS (Toronto Extremity Salvage Scale) surveys calibrated to 6 months postoperatively. Socioeconomic status (SES) was assessed through ADI and SVI scores, categorized into tertiles. Statistical analysis involved Chi-square, Fisher exact tests, and logistic regression using SPSS V26.0. RESULTS: Within the SVI categories for 302 patients, complication percentages were observed as follows: 33.3% in the high vulnerability group, 36.2% in the intermediate vulnerability group, and 42.6% in the low vulnerability group. Similarly, for ADI categories, the rates were 30.7% for the high deprivation group, 48.1% for the intermediate deprivation group, and 40.2% for the low deprivation group. The response rate for the TESS survey was recorded at 17.96%, with 46 out of 256 patients providing feedback. Similarly, the PROMIS-29 survey yielded a participation rate of 17.19%, with 44 out of 256 patients responding. The physical function domain in the PROMIS-29 survey revealed significant differences across ADI tertiles (p=0.035), with higher ADI associated with lower physical function scores, indicating a poorer quality of life. No significant differences were observed in other domains or across SVI tertiles. TESS scores did not vary significantly by SES, indicating uniform functional recovery irrespective of socioeconomic background. DISCUSSION: The minimal differences in patient-reported outcomes highlights the complex interplay of factors influencing postoperative recovery. The significant finding in the physical function domain among higher ADI tertiles suggests potential underreporting of difficulties by lower SES groups, possibly due to cognitive biases shaped by lifelong socioeconomic challenges (2). This underlines the necessity of incorporating socioeconomic awareness into surgical care to identify and address potential barriers to recovery, ensuring equitable patient outcomes. REFERENCES: 1. Mehaffey, J. H., Hawkins, R. B., Charles, E. J., et al. Socioeconomic “Distressed Communities Index” improves surgical risk adjustment. Annals of Surgery, 2020;271:470-474. 2. Hao, Y., Evans, G. W., Farah, M. J. Pessimistic cognitive biases mediate socioeconomic status and children’s mental health problems. Scientific Reports, 2023;13:5191.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.136
GPT teacher head0.360
Teacher spread0.224 · 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.

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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