Implementation of virtual mentoring to optimize collection of patient-reported data in the COVID-19 era: A prospective cohort study with adaptive design.
Bibliographic record
Abstract
e24091 Background: Prior to the COVID-19 pandemic, all patients attending ambulatory clinic at cancer centers in Ontario, Canada completed the Edmonton Symptom Assessment Scale (ESAS), as per governmental cancer agency mandate. At our center, completion was via touch pad, with assistance by clinic volunteers. As of mid-March 2020, clinic appointments were conducted virtually whenever possible, and touch pads removed from clinic. Our purpose here was to explore how these changes impacted the collection of patient-reported outcomes, in particular the recognition of severe symptoms. Methods: We performed a prospective cross-sectional cohort study to test the feasibility of remote completion of the ESAS by patients scheduled for appointments at a weekly surgical oncology clinic at a major cancer center. Patients were identified serially based on date of clinic appointment. Patients in the initial study cohort were asked to complete and return the ESAS virtually (V). Given low completion rates, the ensuing study cohort was asked to complete a hard-copy (HC) ESAS. For the final cohort, we used an adaptive approach, providing remote, personal mentoring by a member of the health care team to support virtual ESAS completion (virtual-mentored, VM). Results: Between May-July 2020, a total of 174 patients were included in the study: 53% were female, and median age was 62 (19-90) yrs. Age, gender and tumor site did not differ between the three cohorts. For the V cohort, 20/43 patients successfully completed and returned the ESAS electronically (completion rate 44%). For the HC cohort, 49/50 completed the form (98%). For the VM cohort (n=78), the completion rate was 74%. Questionnaire completion was not predicted by age, gender or tumor site, although patients who completed the ESAS were more likely to be under active investigation/treatment vs. surveillance (p=.04). Of the 127 ESAS forms completed in all patient cohorts, 117 reported at least 1 symptom score ≥1. There were no significant differences in individual symptom scores (e.g. tiredness, wellbeing-see Table) reported between cohorts. Of all completed forms, 42% had a depression score ≥2 and 27% an anxiety score ≥4, indicating significant psychosocial distress. Conclusions: We have identified significant barriers to the virtual completion of ESAS forms, with lack of predictive variables. The severe degree of psychological distress reported by ̃50% of respondents demonstrates the need for ongoing regular collection and review of these data.[Table: see text]
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".