Evidence map›Paper›PMID 39196508›Full record

ArticleObesity surgery2024

Does Patient's Metabolic and Bariatric Surgery Knowledge Predict Optimal Clinical Outcomes?

Dvir Froylich, Daniella Pinkhasova, Elena Borisover, Eden Gerszman, Edress Khatib, Ahmad Mahamid, Riad Haddad, David Hazzan

Abstract read
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Article in Obesity surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dvir FroylichThe Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. dvirfr7@gmail.com.ORCID 0000-0003-2957-5695
Daniella PinkhasovaThe Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Elena BorisoverDepartment of Surgery, Carmel Medical Center, 7 Michal St., 3436212, Haifa, Israel.
Eden GerszmanThe Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Edress KhatibDepartment of Surgery, Carmel Medical Center, 7 Michal St., 3436212, Haifa, Israel.
Ahmad MahamidThe Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Riad HaddadThe Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
David HazzanDepartment of Surgery, Carmel Medical Center, 7 Michal St., 3436212, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePreoperative evaluation and educational training are required before metabolic and bariatric surgery. This study evaluates patient's comprehension prior to the operation and identifies the relationship between certain sociodemographic parameters and surgery outcomes. MATERIALS AND

methodsAn analysis of patients who completed a preoperative questionnaire and underwent metabolic and bariatric surgery between 2019 and 2021 was performed. The questionnaire evaluated surgery preparation and factors influencing weight loss after surgery.

resultsIn total, 81 patients completed the preoperative questionnaire. Mean age was 44 ± 11.69 years, 63 females (77%). Mean BMI was 42.85 ± 5.72 kg/m

conclusionThe number of factors can influence optimal weight loss following metabolic and bariatric surgery. Identifying groups with certain characteristics and addressing their weaknesses may improve weight loss outcomes.

Indexed as

Bariatric SurgeryObesity, MorbidWeight LossAdultFemaleHealth Knowledge, Attitudes, PracticeHumansIsraelMaleMiddle AgedPatient Education as TopicSurveys and QuestionnairesTreatment OutcomeBariatric surgeryDemographic factorsPatients’ evaluationPreoperative questionnaireWeight loss

Identifiers

PMID39196508

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.