Involving clinic patients in systematic symptom reporting to improve cancer care: Exploring prevalence of sleep disturbances (SD) and fatigue (FAT).
Bibliographic record
Abstract
68 Background: SD and FAT occur in 30-50% of cancer patients. Patient-reported outcome measure surveys are avenues through which healthcare providers (HCP) can receive symptom-related clinically relevant information directly from patients, and engage them in their own care plan. By asking patients to report symptoms rapidly through tablet/computer-based technology, HCPs can involve patients in the delivery of care. Methods: In a pilot study evaluating utility of systematic symptom reporting, 336 adult cancer patients across all stages and disease sites who were attending outpatient cancer clinics at Princess Margaret Cancer Centre (PMCC) (Toronto, Canada) completed electronic tablet-administered secure surveys on SD (Insomnia Severity Index) and FAT patterns (FACT-fatigue). These tools measured both symptom severity and interference with function. Results: With a median age of 59 (19-91) years, 55% female, across a broad distribution of cancer sites, 56% of our sample reported moderate to very severe (MTVS) SD over the last 7 days: 31% had MTVS difficulty falling asleep; 43% had MTVS difficulties staying asleep; 36% had MTVS problems waking up too early. While 62% who had MTVS SD were not distressed by their SD, 95% who were distressed by their SD met the criteria of MTVS SD. 78% of patients had any level of FAT over the last 7 days, with 40% reporting MTVS FAT. While 40% who had MTVS FAT were still able to perform their usual activities, 67% of patients who were not able to do their usual activities had MTVS FAT. Conclusions: Across all stages and disease sites of cancer patients at PMCC, the prevalence of SD and FAT was both high. Severity and interference with function by FAT and SD were often distinct and non-overlapping. Involving patients in the systematic evaluation of symptoms, particularly using newer tablet-based technology within the clinic, was feasible. Through the use of patient reported electronic applications, patients could easily and systemically report their symptoms in real-time. FAT management has always been a high priority at our institution. However, based on our results, a cancer center-wide self-management plan is being considered for SD.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".