Re: The Combined Use of Atypical Antipsychotics and Cognitive-Behavioural Therapy in Schizophrenia
Bibliographic record
Abstract
We read with interest Dr Duggal’s letteron the combined use of atypical anti-psychotics (AAs) and cognitive-behavioural therapy (CBT) inschizophrenia (1). Dr Duggal reportsthat his patient showed a reduction of54% in symptom severity as indexed bythe Positive and Negative SyndromeScale(PANSS).Hisbaselinescorewhenunmedicatedwas129andvariedfrom83to 102 with medication; when medica-tion was combined with CBT, hisPANSS score was 59.Dr Duggal’s finding is consistent with,and adds to, the existing literature on thecombined use of second-generationantipsychotics (SGAs) and CBT. OurSymptom-Specific Group TreatmentProgram (conducted at Bronx Psychiat-ric Center) was designed to match the 5symptom dimensions of the PANSS:positive,negative,activation,dysphoria,andautisticpreoccupation(2).Wefoundthatpatientsattendingsymptom-specificgroups in addition to receiving standardmedications showed an additional 22%decrease in symptom severity whencompared with a group of patientsreceiving standard medications alone(3).We question only Dr Duggal’s specula-tion that “AAs potentiate CBT.” CBThas been found to be useful in patientswho receive standard neuroleptics,including those in our sample, as well asin patients receiving SGAs. Further, itmay well be that SGAs and CBT do notpotentiate one another but that theireffects are additive. The 2 treatmentmodalities may be targeting differentfacets of schizophrenia. For example,CBT may teach or remediate copingskills, cognitive functions, and socialadeptnessimpairedduringacutepsycho-sis; standard neuroleptics target positivesymptoms, and SGAs target positive,negative, activation, dysphoria, andautistic preoccupation symptoms.More work is needed to better under-stand whether the interaction betweenantipsychotics and CBT is additive orsynergistic, as well as how CBT workswith different antipsychotics.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".