Episodic Cancer Pain: Patient Reporting, Prevalence, and Clinicodemographic Associations at Initial Cancer Pain Clinic Assessment
Bibliographic record
Abstract
Background . Better <?tex id="Q1" staff-cmt="As per journal style for canada state should be abbreviated. Hence we ignored the author comment. Please check."?>understanding of the episodic cancer pain (CP) spectrum, including pains that occur in addition to its conventionally defined breakthrough CP (BTcP) and incident CP (IcP) components, may inform CP assessment and management. This study aimed to determine the prevalence of episodic patient-reported CP and the prevalence and associations of study-defined BTcP (S-BTcP) and IcP (S-IcP) in patients with CP. Methods . In a cross-sectional study at their first CP clinic attendance, participants with CP had the following assessments: Brief Pain Inventory (BPI); Pain Management Index (PMI), with PMI-negative status indicating undertreatment; standardized neuropathic pain component (NPC) status; S-BTcP (no trigger identified) and S-IcP (trigger identified) status, based on a preceding 7-day history of transitory pain flares distinct from background pain, and BPI-Worst or BPI-Now pain intensity ≥ 4. Clinicodemographic variables’ association with S-BTcP and S-IcP was examined in logistic regression analyses. Results . Of 371 participants, 308 (83%) had episodic CP by history alone; 140 (37.7%) and 181 (48.8%) had S-BTcP and S-IcP, respectively. Multivariable analyses demonstrated significant (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.05</mml:mn></mml:math>) associations (odds ratios: 95% CIs) for 6 variables with S-BTcP: head and neck pain location (2.53; 1.20–5.37), NPC (2.39; 1.34–4.26), BPI average pain (1.64; 1.36–1.99), abdominal pain (0.324; 0.120–0.873), S-IcP (0.207; 0.116–0.369), and PMI-negative status (0.443; 0.213–0.918). Similar independent associations (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.05</mml:mn></mml:math>) occurred for S-IcP with NPC, BPI average pain, and PMI-negative status, in addition to radiotherapy, S-BTcP, soft tissue pain, and sleep interference. Conclusions . Episodic or transient patient-reported CP flares often do not meet the more conventional criteria that define BTcP and IcP, the principal episodic CP types. Both BTcP and IcP occur frequently and both are associated with a NPC, higher pain intensity, and less opioid underuse in the management of CP. Further studies are warranted to both better understand the complex presentations of episodic CP and inform its classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".