Turning Patients’ Open-Ended Narratives of Chronic Pain Into Quantitative Measures: Natural Language Processing Study
Bibliographic record
Abstract
BACKGROUND: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience. OBJECTIVE: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives. METHODS: Patients' narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP is a novel approach in this field, our goal was to demonstrate its ability to extract measures that relate to commonly used tools, such as validated pain questionnaires and rating scales, including the numerical rating scale and visual analog scale. RESULTS: First, we showed that patients' utterances were significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we found that the semantic distances between patients' utterances and anchor sentences related to quality of life were strongly correlated with reported CLBP intensity on the numerical rating and visual analog scales. Consistently, we observed significant differences between individuals with low and high pain levels. CONCLUSIONS: Although our small sample size limits the generalizability of these findings, the results provide preliminary evidence that NLP can be used to quantify the subjective experience of chronic pain and may hold promise for clinical applications.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".