Allostatic load as a predictor of response to repetitive transcranial magnetic stimulation in treatment resistant depression: Research protocol and hypotheses
Bibliographic record
Abstract
Treatment resistant depression is challenging because patients who fail their initial treatments often do not respond to subsequent trials and their course of illness is frequently marked by chronic depression. Repetitive transcranial magnetic stimulation (rTMS) is a well-established treatment alternative, but there are several limitations that decreases accessibility. Identifying biomarkers that can help clinicians to reliably predict response to rTMS is therefore necessary. Allostatic load (AL), which represents the 'wear and tear' on the body and brain which accumulates as an individual is exposed to chronic stress could be an interesting staging model for TRD and help predict rTMS treatment response. We propose an open study which aims to test whether patients with a lower pre-treatment AL will have a stronger antidepressant response to 4 week-rTMS treatment. We will also assess the relation between healthy lifestyle behaviors, AL, and rTMS treatment response. Blood samples for AL parameters will be collected before the treatment. The AL indices will summarize neuroendocrine (cortisol, Dehydroepiandrosterone), immune (CRP, fibrinogen, ferritin), metabolic (glycosylated hemoglobin, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, uric acid, body mass index, waist circumference), and cardiovascular (heart rate, systolic and diastolic blood pressure) functioning. Mood assessment (Montgomery-Åsberg Depression Rating Scale and Inventory of Depressive symptomatology) will be measured before the treatment and at two-week intervals up to 4 weeks. With the help of different lifestyle questionnaires, a healthy lifestyle index (i.e., a single score based on lifestyle factors) will be created. We will use linear and logistic regressions to assess AL in relation to changes in mood score. Hierarchical regression will be done in order to assess the association between AL, healthy lifestyle index and mood score. Long-lasting and unsuccessful antidepressant trials may increase the chance of not responding to future trials of antidepressants and it can therefore increase treatment resistance. It is essential to identify reliable biomarkers that can predict treatment responses.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".