Evidence map›Paper›PMID 32083690›Full record

ArticleJAMA network open2020

Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations.

Kelly A Speth, Alfred P Yoon, Lu Wang, Kevin C Chung, FRANCHISE Group

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
  2. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Kelly A SpethDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor.
Alfred P YoonSection of Plastic Surgery, Michigan Medicine, Ann Arbor.
Lu WangDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor.
Kevin C ChungSection of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor.
FRANCHISE Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Optimal treatment for traumatic finger amputation is unknown to date. Objective: To use statistical learning methods to estimate evidence-based treatment assignment rules to enhance long-term functional and patient-reported outcomes in patients after traumatic amputation of fingers distal to the metacarpophalangeal joint. Design, Setting, and Participants: This decision analytical model used data from a retrospective cohort study of 338 consenting adult patients who underwent revision amputation or replantation at 19 centers in the United States and Asia from August 1, 2016, to April 12, 2018. Of those, data on 185 patients were included in the primary analysis. Exposures: Treatment with revision amputation or replantation. Main Outcomes and Measures: Outcome measures were hand strength, dexterity, hand-related quality of life, and pain. A tree-based statistical learning method was used to derive clinical decision rules for treatment of traumatic finger amputation. Results: Among 185 study participants (mean [SD] age, 45 [16] years; 156 [84%] male), the median number of fingers amputated per patient was 1 (range, 1-5); 115 amputations (62%) were distal to the proximal interphalangeal joint, and 110 (60%) affected the nondominant hand. On the basis of the tree-based statistical learning estimates, to maximize hand dexterity or to minimize patient-reported pain, replantation was found to be the best strategy. To maximize hand strength, revision amputation was the best strategy for patients with a single-finger amputation but replantation was preferred for all other injury patterns. To maximize patient-reported quality of life, revision amputation was the best approach for patients with dominant hand injuries, and replantation was the best strategy for patients with nondominant hand injuries. Conclusions and Relevance: The findings suggest that the approach to treating traumatic finger amputations varies based on the patient's injury characteristics and functional needs.

Indexed as

Amputation, TraumaticClinical Decision RulesFinger InjuriesAdultDecision TreesEvidence-Based MedicineFemaleFingersHumansMaleMiddle AgedModels, StatisticalRetrospective Studies

Identifiers

PMID32083690
PMCPMC7043191

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.