Decoding Back Pain: Diagnostic Reasoning to Precision Interventions – Invited Lecture at 2nd Delhi Pain Summit (2025)
Bibliographic record
Abstract
This lecture introduces a pragmatic WHERE–WHY diagnostic framework for decoding back pain in daily practice: WHERE is the pain coming from? WHY is that structure painful now? Using this framework, the presentation systematically reviews the main spinal and extra-spinal pain generators—disc, facet joints, sacroiliac joint, muscles, ligaments, fascia, hip, nerve root and spinal canal pathology—and correlates them with clinical history, physical examination, and selected diagnostic blocks. Key themes include: Limitations of the blanket term “nonspecific low back pain” and the mismatch between imaging abnormalities and clinical symptoms. Practical clinical patterns and bedside tests that suggest discogenic, facet, sacroiliac, myofascial, hip-related or radicular pain. Judicious use of diagnostic blocks to confirm or refute suspected pain generators and to guide interventional planning. The growing recognition of central sensitisation and nociplastic low back pain, including scenarios where interventional pain procedures or surgery are unlikely to help. Case-based examples of failed surgery or failed interventions where the underlying diagnostic reasoning was incomplete or incorrect. The slide deck is intended for pain physicians, anaesthesiologists, orthopaedic surgeons, physiatrists and other clinicians who wish to refine their diagnostic reasoning and align their choice of intervention with the underlying pain mechanism. Learning Objectives After reviewing this presentation, clinicians will be able to: Describe why most low back pain was historically labelled “nonspecific” and what has changed in modern pain medicine. Apply the WHERE–WHY framework to structure diagnostic reasoning in chronic and subacute low back pain. Distinguish common nociceptive pain generators (disc, facet, SIJ, myofascial, hip, spinal canal) based on history, examination and targeted investigations. Recognise the role and limitations of imaging in the evaluation of back pain. Use diagnostic blocks more systematically, including recognising potential pitfalls and false positives. Identify clinical features of neuropathic and nociplastic low back pain and understand when non-interventional strategies should be prioritised. How to Cite This Presentation Vancouver style: Das G. Decoding Back Pain: Diagnostic Reasoning to Precision Interventions. Invited lecture presented at: 2nd Delhi Pain Summit, Global Pain School Annual Conference; 2025 Nov 9; New Delhi, India. DOI: 10.5281/zenodo.17623859 APA 7th edition: Das, G. (2025, November 9). Decoding Back Pain: Diagnostic Reasoning to Precision Interventions [Conference presentation]. 2nd Delhi Pain Summit, Global Pain School Annual Conference, New Delhi, India. DOI: 10.5281/zenodo.17623859 Links and Resources Slides, extended summary, and additional resources are available at Daradia: The Pain Clinic: 👉 https://daradia.com/decoding-back-pain Learn more about Daradia’s clinical work and educational programmes in pain medicine: 👉 https://daradia.com/
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".