Clinical effectiveness of switching to right lateral orbitofrontal cortex-TMS after failure of sequential bilateral dorsolateral prefrontal-TMS in major depression
Bibliographic record
Abstract
Background When patients with major depression fail to respond to TMS using a conventional dorsolateral prefrontal cortex (DLPFC) protocol, several published case series have suggested that switching to or adding-on 1Hz right lateral orbitofrontal cortex (R-LOFC) TMS may succeed. However, many of these case series are hampered by relatively short courses of treatment (i.e., <25 sessions) for either the DLPFC, the R-LOFC, or both protocols. Methods Here we report clinical outcomes for a case series of N=54 patients in a community setting who underwent a full course of 36 sessions of sequential bilateral (SBL) DLPFC-TMS (right/left, 1Hz/20Hz, 60s on 30s off/2s on 4s off, 360 pulses/1200 pulses, both 120% RMT) without response, then completed a full course of another 36 sessions of 1 Hz-R-LOFC-TMS (1Hz, 60s on 30s off, 360 pulses). Results Following the R-LOFC course, 15/54 (27.8%) achieved response and 9/54 (16.7%) achieved remission on PHQ-9, with a mean score improving from 14.2±4.1 to 9.5±5.3 points. Responders showed a distinctive discontinuity in response trajectory immediately following the switch from DLPFC to R-LOFC, with a significant sharp drop in symptoms suggesting causal effect. Conclusion Switching to 1 Hz-R-LOFC-TMS yields a sharp change in response trajectory in a subset of SBL-DLPFC-TMS unresponsive patients, implying a distinctive therapeutic mechanism rather than the accumulative effects of additional sessions. Future randomized controlled studies may establish definitive efficacy for 1 Hz-R-LOFC-TMS in depression among non-responders to conventional DLPFC-TMS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".