Classification of Qualitative Fieldnotes Collected During Quantitative Sensory Testing: A Step Towards the Development of a New Mixed Methods Approach in Pain Research
Bibliographic record
Abstract
PURPOSE: Quantitative sensory testing (QST) is a standardized method to assess somatosensory function. The collection of qualitative information, during the QST procedure, could be an interesting way to facilitate the characterization of altered sensory perception and the identification of different pain phenotypes. The aims of this study were 1) to classify qualitative fieldnotes of sensory abnormalities collected during an independent QST study, and 2) to generate a qualitative interview guide that could be included in the traditional QST procedure as a step towards the implementation of a mixed methods approach. PATIENTS AND METHODS: QST data were collected from 48 chronic neuropathic pain patients treated with spinal cord stimulation (SCS). Three body areas, with or without SCS, were tested: the painful limb targeted by SCS, the contralateral area, and the ipsilateral upper limb. After each trial of each QST modality, patients were encouraged to report any sensory abnormalities they could identify with a pain quality scale or using their own words. RESULTS: Qualitative self-reported sensory abnormalities were dichotomized into two groups: altered sensory intensities and altered sensory perceptions. Altered sensory intensities were classified as sensory loss or sensory gain subgroups. Altered sensory perceptions were classified as paresthesia and dysesthesia subgroups Overall, 630 qualitative fieldnotes of altered sensations were collected: 385 on the painful limb, 173 at the contralateral area, and 72 at the ipsilateral upper limb. Based on these qualitative data, we propose a standardized method to collect qualitative data involving 9 open- and close-ended questions and 21 codes. CONCLUSION: Our findings have highlighted the value of qualitative sensory evaluation during QST and constitute an important milestone in the development of a mixed methods protocol in phenotyping research.
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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 | 0.302 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".