Rapid and slow responders to eating disorder treatment: A comparison on clinically relevant variables
Bibliographic record
Abstract
OBJECTIVE: Speed of response to eating disorder treatment is a reliable predictor of relapse, with rapid response predicting improved outcomes. This study investigated whether rapid, slow, and nonresponders could be differentiated on clinically relevant variables, and possibly identified prior to treatment. METHOD: Female patients (N = 181) were classified as rapid, slow, or nonresponders based on the speed and magnitude with which they interrupted their bingeing and/or vomiting symptoms, and were compared on eating disorder behaviors and psychopathology and general psychopathology. RESULTS: The rapid response group was marginally older and had a slightly shorter course of treatment than the slow response group. The rapid response group also had significantly fewer pretreatment binge episodes, and a longer course of treatment than the nonresponse group. However, the three response groups were not significantly different on any other examined variables. DISCUSSION: The only pretreatment variable that differentiated response groups was symptom frequency, in that rapid responders had fewer binge episodes than nonresponders. No pre-existing variables differentiated rapid and slow response. Given that few individual pre-existing differences that might account for speed of response were identified, the clinical importance of facilitating a rapid response to treatment for all patients is discussed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".