Latent Profile Analysis of Canadian Military Veterans With Chronic Pain Identifies 5 Meaningful Classes Through Self-Report Measures
Bibliographic record
Abstract
The purpose of this study was to identify meaningful response patterns in self-report survey data collected from Canadian military veterans with chronic pain and to create an algorithm intended to facilitate triage and prioritization of veterans to the most appropriate interventions. An online survey was presented to former members of the Canadian military who self-identified as having chronic pain. Variables collected were related to pain, physical and mental interference, prior traumatic experiences, and indicators from each of the 7 potential drivers of the pain experience. Maximum likelihood estimation-based latent profile analysis was used to identify clinically and statistically meaningful profiles using the 7-axis variables, and classification and regression tree (CRT) analysis was then conducted to identify the most parsimonious set of indicators that could be used to accurately classify respondents into the most relevant profile group. Data from N = 322 veterans were available for analysis. The results of maximum likelihood estimation-based latent profile analysis indicated a 5-profile structure was optimal for explaining the patterns of responses within the data. These were: Mood-Dominant (13%), Localized Physical (24%), Neurosensory-Dominant (33%), Central-Dominant with complex mood and neurosensory symptoms (16%), and Trauma- and mood-dominant (14%). From CRT analysis, an algorithm requiring only 3 self-report tools (central symptoms, mood screening, bodily coherence) achieved 83% classification accuracy across the 5 profiles. The new classification algorithm requiring 16 total items may be helpful for clinicians and veterans in pain to identify the most dominant drivers of their pain experience that may be useful for prioritizing intervention strategies, targets, and relevant health care disciplines. PERSPECTIVE: This article presents the results of latent profile (cluster) analysis of responses to standardized self-report questionnaires by Canadian military veterans with chronic pain. It identified 5 clusters that appear to represent different drivers of the pain experience. The results could be useful for triaging veterans to the most appropriate pain care providers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".