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Record W4361001663 · doi:10.1037/hea0001279

Health change awareness and its association with weight loss following bariatric surgery.

2023· article· en· W4361001663 on OpenAlexaff
Michael Wolfe, Todd J. Williams, Elizabeth N. Dewey, James E. Mitchell, Alfons Pomp, Bruce M. Wolfe

Bibliographic record

VenueHealth Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversité de Montréal
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesOregon Health and Science University
KeywordsPsycINFOMedicineWeight lossConcordanceBody mass indexWeight changeRecallLongitudinal studyGerontologyMEDLINEDemographyObesityPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Patients' ability to judge health change over time has important clinical implications for treatment, but is understudied in longitudinal contexts with meaningful health change. We assess patients' awareness of health change for 5 years following bariatric surgery, and its association with weight loss. METHOD: = 2,027). Perceived health change for each year was assessed by comparing it to self-reports of health on the SF-36 health survey. Participants were categorized as concordant when perceived and actual self-reported health change corresponded, and as discordant when they did not correspond. RESULTS: Year-to-year concordance between perceived and actual self-reported health change occurred less than 50% of the time. Discordance between perceived and actual health was associated with weight loss following surgery. Discordant-positive participants who perceived their health change as more positive than was warranted lost more weight post-surgery and thus had lower body mass index scores than concordant participants. Conversely, discordant-negative participants who perceived their health as worse than what was warranted lost less weight post-surgery and thus had higher body mass index scores. CONCLUSIONS: These results suggest that recollection of past health is generally poor and can be biased by salient factors during recall. Clinicians are advised to use caution when retrospective judgments of health are utilized. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.397
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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