Crosswalk between HRSD and MADRS outcomes for rTMS in patients with depression
Bibliographic record
Abstract
BACKGROUND: The Hamilton Rating Scale for Depression (HRSD) and the Montgomery-Åsberg Depression Rating Scale (MADRS) are the two most common clinician-rated scales to quantify depression symptom change in repetitive transcranial magnetic stimulation (rTMS) trials. However, it is unclear how the values of one scale translate to the other. Being able to translate scores between these scales could allow for aggregating rTMS clinical trial data. METHODS: Clinical data from two randomised rTMS clinical trials (FOURD and CARTBIND, total N=380) were pooled. We used five crosswalk models: (1) a pharmacotherapy equipercentile model, (2) an rTMS equipercentile model, (3) a linear regression model, (4) a random forest (RF) regression model and (5) a support vector regression (SVR) model. Model performance was benchmarked using the root mean square error (RMSE). RESULTS: The linear regression model demonstrated the best performance (RMSE: 2.66-4.82), though the SVR model's performance was slightly worse but comparable (RMSE: 2.69-5.32). The RF regression model generally performed worst (RMSE: 2.70-5.20). The rTMS equipercentile model's performance was intermediate (RMSE: 2.69-5.32) in the primary analysis but achieved superior performance and demonstrated less bias in the additional analysis. INTERPRETATION: MADRS and HRSD scores from rTMS trials can be accurately converted between each other. The optimal model was the newly developed equipercentile model, though the results of the SVR model were promising. Nevertheless, independent external replication is required to demonstrate the external validity of these findings. TRIAL REGISTRATION NUMBER: FOURD: NCT02998580; CARTBIND: NCT02729792.
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 | 0.042 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".