Improving quality of care by obtaining patient-reported outcomes (PRO)-CTCAE chemotoxicities using tablet technology in daycare (DC) waiting rooms.
Bibliographic record
Abstract
165 Background: In a busy chemo DC, any efficient means of tracking important chemotoxicities can improve quality of care. The study goal was to evaluate whether tablet technology available in a DC waiting room is able to capture prevalent and severe toxicities associated with chemotherapy using the patient reported outcome (PRO) - common toxicity criteria for adverse events (CTCAE). Methods: This cross-sectional PRO-CTCAE study of 160 adult solid/hematologic cancer outpatients of all stages, who were undergoing chemotherapy (CT), focused on common chemotoxicities captured using touchscreen tablets in the DC waiting room of Princess Margaret Cancer Centre (Toronto, CA). Individual health scores from the EQ-5D VAS tool and the prevalence of AEs experienced by cancer patients within the past seven days were captured. Symptoms that were listed as moderate to very severe were considered significant. Results: Across a wide range of tumours and patients on intravenous CT, the median age (range) was 56 (19-88) years; 38% were males.Patients reported a median (range) health score (100 = best health possible, 0 = worst) of 70 (4-100). The severity offiveprevalent, key side-effects of CT were tabulated (Table). 59% of patients felt their fatigue interfered significantly with their daily activities, and 30% felt decreased appetite interfered significantly. 32% experienced nausea occasionally to almost constantly. Conclusions: The common symptoms of CT were captured FEASIBLY, and found to be highly prevalent in this CT-treated population. Capturing additional symptom prevalence outside of the 7-day time frame may be important from a clinical standpoint. Administration of PRO-CTCAE instrument through tablet technology may be an excellent method to help collect such data systematically and reliably. Updated data on 300 patients will be presented at the meeting. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".