An Integrative Approach to Clinical Decision-Making for Treating Patients With Binge-Eating Disorder
Bibliographic record
Abstract
Transtheoretical integrative decision-making models help clinicians to use patient factors that are known to predict outcomes in order to inform individualized treatment. Patient factors with a strong evidence base include: functional impairment, social support and interpersonal functioning, complexity and comorbidity, coping style, level of resistance, and level of subjective distress. Among those with binge-eating disorder (BED), patient factors have not been extensively characterized relative to norms or other clinical samples. We used an integrative decision-making model of these six patient factor domains related to patient outcomes to characterize a sample of 424 adults seeking treatment for BED. Data were from medical charts, a demographics questionnaire, and validated psychometric scales. We then compared these data to published data from normative and other eating disorder (ED) samples. Results showed that the average patient with BED: (1) was significantly more functionally impaired compared to non-clinical norms but somewhat less impaired than other patients with ED, (2) demonstrated clinically significant problems in social support and interpersonal functioning, (3) presented with complex comorbid pathology and high levels of chronicity, (4) used a more internalizing coping style compared to the norm and other ED samples, (5) had low levels of resistance to interventions, and (6) experienced a moderately high level of subjective distress indicating good motivation for treatment. Corresponding recommendations to these findings are that the average patient with BED should be provided higher intensity treatment that is longer in duration, interpersonally focused, directive in nature, and emphasizing self-reflection and insight. Despite the nomothetic nature of the findings, clinicians are encouraged to assess these patient domains when developing an ideographic case conceptualization and to tailor precision treatment to the individual patient with BED.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".