Obesity bias: How can this underestimated problem affect medical decisions in healthcare? A systematic review
Bibliographic record
Abstract
INTRODUCTION: Obesity is often labeled as a physical characteristic of a patient rather than a disease and it is subject to obesity bias by health providers, which harms the equality of healthcare in this population. OBJECTIVE: Identifying whether obesity bias interferes in clinical decision-making in the treatment of patients with obesity. METHODS: A systematic review of observational studies published between 1993 and 2023 in MEDLINE, Embase, and Cochrane Library on obesity bias and therapeutic decisions was carried out. The last search was conducted on June 30, 2023. The main outcome was the difference between clinical decisions in the treatment of individuals with and without obesity. The Newcastle-Ottawa scale for observational studies was used to assess for quality. After the selection process, articles were presented in narrative and thematic synthesis categories to better organize the descriptive analysis. RESULTS: Of the 2546 records identified, 13 were included. The findings showed fewer screening exams for cancer in patients with obesity, who were also susceptible to less frequent pharmacological treatment intensification in the management of diabetes. Women with obesity received fewer pelvic exams and evidence of diminished visual contact and physician confidence in treatment adherence was reported. Some studies found no disparities in treatment for abdominal pain and tension headaches between patients presented with and without obesity. CONCLUSION: The presence of obesity bias has negative effects on medical decision-making and on the quality of care provided to patients with obesity. These findings reveal the urgent necessity for reflection and development of strategies to mitigate its adverse impacts. (The protocol was registered with the international prospective register of systematic reviews, PROSPERO, under the number CRD42022307567).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.182 | 0.459 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".