Six-month alleviation from negative schizophrenic symptoms after repetitive transcranial magnetic stimulation (rTMS): a single case study report
Bibliographic record
Abstract
UVOD: Cilem nasi prace bylo ověřit (a) ucinnost vysokofrekvencni rTMS nad oblasti leveho prefrontalniho kortexu v lecbě negativnich přiznaků schizofrenie a (b) zjistit, zda připadný terapeutický efekt přetrva po dobu alespoň sesti měsiců od ukonceni stimulace. METODIKA: Muž (42 let) s paranoidni schizofrenii a výraznými negativnimi přiznaky podstoupil třitýdenni lecbu 10Hz rTMS. Pozitivni, negativni a depresivni přiznaky onemocněni byly hodnoceny před a po stimulacni lecbě a v intervalu sesti měsiců po ukonceni lecby rTMS. VÝSLEDKY: Lecba rTMS vedla ke sniženi o 35% celkoveho skore PANSS, o 23% pozitivniho subskore PANSS, o 40% negativniho subskore PANSS, o 43% v SANS a k žadne změně v CDSS (Calgary Depression Scale for Schizophrenia). Procentualni změny jednotlivých skal (oproti skore po lecbě rTMS) po sesti měsicich od ukonceni lecby byly nasledujici: pokles o 4% v celkovem PANSS, o 6% v negativni subskale PANSS, o 5% v SANS, vzestup o 14% v pozitivni subskale PANSS, žadna změna nebyla nalezena v CDSS. SHRNUTI: Vysokofrekvencni rTMS aplikovana nad oblasti leveho prefrontalniho kortexu vedla k ustupu negativnich přiznaků na dobu alespoň sesti měsiců od ukonceni lecby u pacienta se schizofrenii.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".