NOCICEPTIVE AND NEUROPATHIC PAIN IN PATIENTS WITH LUNG CANCER: A COMPARISON OF PAIN QUALITY DESCRIPTORS
Bibliographic record
Abstract
Predictive validity of each word from the McGill Pain Questionnaire (MPQ) has not been investigated independent of pain etiology. The purpose of this study was to explore differences in the words used to describe nociceptive and neuropathic pain. Patients with lung cancer (N = 123) selected words from the 78 MPQ pain quality descriptors and indicated the corresponding pain site for each word. Using only the MPQ pain location and the disease and treatment data abstracted from medical records, each pain site was classified as nociceptive, neuropathic (etiology). Pain etiology and quality descriptors were tested for proportional differences. Of the 457 pain sites, 343 were classified as nociceptive (75%), 114 as neuropathic (25%). Lacerating, stinging, terrifying and suffocating were selected for a significantly larger proportion of nociceptive sites whereas throbbing, aching, numb, tender, punishing, pulling, tugging, pricking, punishing, miserable, and nagging were selected for a larger proportion of neuropathic sites. Interestingly, several pain quality descriptors (burning, shooting, flashing, tingling, itching, and cold) previously associated with neuropathic pain did not distinguish between neuropathic and nociceptive pain etiologies in this lung cancer sample. Infrequent selection of most MPQ words and lack of neurological exam data in the categorizing scheme are possible explanation for inconsistency with previous literature. Prospective investigations are needed to validate pain quality descriptors for nociceptive and neuropathic types of pain.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".