Level C· Early human research exploring benefitsRetrospective StudyEurope PMCOpen access

Establishment and validation of an interactive web-based calculator for predicting postoperative functional recovery in metatarsal fracture patients: A LASSO regression model approach

Xiao Q., Hou G., Liu S., Zhou S., Chen W., Zhang Y.

Retrospective Study with a reported sample of 555 on Ankle & Foot, published in PLoS One (2025) — summary generated from the PubMed abstract.

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Level C· Early human research exploring benefitsEvidence level of this study

Early human evidence such as case series or small samples is exploring possible benefits.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
Read the A–D evidence level guide

This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Retrospective Study
Journal
PLoS One (2025)
Reported sample size
555
Source database
Europe PMC
PMID
40460102
PMCID
PMC12132955
DOI
10.1371/journal.pone.0323609

Abstract (original English)

Background Metatarsal fractures rank among the ten most common fractures.Comprehensive studies on postoperative functional recovery remain limited. A reliable predictive model for recovery outcomes is essential for optimizing patient care. Objective To develop and validate a predictive model for postoperative functional recovery in metatarsal fracture patients and implement it as an interactive web-based calculator. Methods This retrospective study included 555 metatarsal fracture patients (2018-2022), with 425 in the training cohort and 130 in the validation cohort. The outcome variable was postoperative recovery as assessed by the AOFAS midfoot scoring system. LASSO regression identified significant predictors of recovery,the selected variables underwent binary logistic regression analysis to identify independent risk factors. A prediction model was constructed using the training cohort and visualized through a nomogram. Model validation was performed internally through bootstrapping and externally using the validation cohort. The model was implemented as an interactive web calculator using R Shiny. Results At final follow-up, 71.71% of patients achieved good recovery (AOFAS score >80). The model identified ten independent risk factors, including residence location, smoking status, obesity, rehabilitation training, educational level, age, injury mechanism, infection, and anem

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • Without an adequate control group, treatment effects cannot be separated from other factors.

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

How we grade evidence
Metatarsal BonesHumansPostoperative PeriodNomogramsRisk FactorsRetrospective StudiesRecovery of FunctionInternetAdultAged

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