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◆ Frontiers in Psychiatry2026-03-16· Psychology

A transdiagnostic conflict-square algorithm: a four-node computational framework for psychotherapy and functional diagnosis

Eik Niederlohmann

原始摘要(英文原文)· Original abstract
Clinical services need more than categorical labels to guide safe dosing, maintain alliance, and plan functional recovery. The Conflict-Square Algorithm (CSA) offers a compact bedside grammar for moment-to-moment decisions during psychotherapy. Clinicians track four observable signals-defense, anxiety/affect tolerance, progression, and superego/shame-and gate intervention intensity by three safety thresholds (A-C). Each clinically meaningful moment is summarized as one auditable episode line in plain language: trigger, observable response, threshold, action, and expected functional impact (Mini-ICF-APP). To strengthen reproducibility, we provide i) operational threshold definitions with observable markers and common misclassification errors, grounded in established anxiety-channel descriptions [striated muscle, smooth muscle, and cognitive-perceptual disruption (CPD)]; ii) a short scope and contraindication checklist; iii) several consecutive worked micro-episodes demonstrating node shifts, threshold transitions, and dose modulation over time; and iv) a minimal machine-readable schema plus a threshold-gating state diagram. As a proof-of-concept feasibility demonstration, we report aggregate coding statistics from three published psychotherapy training videos distributed by the ISTDP Institute (transcribed for analysis with written permission; N = 2,809 speaker turns) using a three-label therapist intervention mapping (invite progression/defense work/anxiety regulation) aligned with CSA nodes. CSA is presented as a teachable, testable decision framework-not as a validated diagnostic instrument-and we outline a pragmatic validation program (rater agreement, safety-rule adherence, usability, and functional outcomes) and future multimodal extensions (e.g., optional physiological monitoring for biofeedback and threshold detection).
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