Bibliographic record
Abstract
The obesity risk assessment concept is developed after considering the increased risk of obesity and the concomitant conditions arising due to obesity. The treatment of obesity is very crucial and proper awareness and diagnosis play the important role in treating obesity. WHO declared BMI as a measure of obesity; however BMI and waist circumference as screening tools to estimate obesity and related potential risk have their shortcomings. These parameters lack sensitivity and specificity when applied to individuals to complete risk assessment related to obesity. Edmonton Obesity Staging System (EOSS) is a clinical staging system that effectively captures the severity of obesity and its factors complicating the management. However, EOSS and other such clinical staging systems are not patient-oriented and are difficult to understand for a layperson. The available staging systems do not classify obesity on the basis of the presence and severity of risk factors, comorbidities, and functional limitations. Hence, we developed an obesity risk assessment scale, which is based on EOSS; but, is patient-oriented and allows patients to understand their level of obesity, the risks associated with it and provides the clinical expertise which will guide them to appropriate obesity management. As it is a novel concept, the concept validation is performed along with a Delphi round where the questionnaire and weightage for each respective question is finalized. The strengths of this obesity risk assessment scale include the simple nature of questions, scoring system, patient-facing tool, and treatment guidance. Future studies are required to carry out the clinical efficacy, reliability, and validity of this method.
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 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".