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Record W4367395064 · doi:10.1007/s11739-023-03263-2

Clinical evaluation of patients living with obesity

2023· review· en· W4367395064 on OpenAlexaboutno aff
Laurence J. Dobbie, Cláudia Matta-Coelho, James Crane, Barbara McGowan

Bibliographic record

VenueInternal and Emergency Medicine · 2023
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObesityHealth careTerminologyMotivational interviewingPublic healthManagement of obesityFamily medicineWeight lossNursingIntervention (counseling)Pathology

Abstract

fetched live from OpenAlex

Obesity is a significant public health concern which is implicated in cardiometabolic disease, mechanical complications and psychiatric sequelae. BMI is currently used for diagnosis; however, it has limited sensitivity for adiposity in certain circumstances. This has led to the development of risk stratification tools like the Edmonton Staging criteria and the Kings Obesity Staging Criteria: these facilitate and guide comprehensive obesity-related complication assessment. Healthcare professionals working within obesity clinics should adopt evidence-based communication strategies, including shared decision-making, motivational interviewing, and realistic goal setting. It is also vital to avoid weight-stigmatising terminology in all aspects of care, as this can negatively impact patients. Primary care plays an essential part in obesity care and should work to promptly identify cases, initiate treatment and forward on to specialist services where appropriate. Clinical evaluation of the patient living with obesity should take a holistic approach and involve input from bariatric physicians, dietitians, psychologists, and bariatric surgeons, wider members of the multi-disciplinary team should be involved where needed. Clinicians should take a detailed history, examination and order laboratory tests to investigate for complications. Overall, with appropriate evaluation, these assessments can guide patient management and facilitate long-term improvement in health.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.467
GPT teacher head0.629
Teacher spread0.162 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2023
Admission routes1
Has abstractyes

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