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Abstract PO-005: Barriers to implementation of virtual collection of patient-reported data in the COVID-19 era

2020· article· en· W3096456848 on OpenAlexaffabout
Karineh Kazazian, Wendy Johnston, Jessica Bogach, Carol J. Swallow

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineAnxietyAmbulatoryDepression (economics)CohortDistressFamily medicineHospital Anxiety and Depression ScaleCoronavirus disease 2019 (COVID-19)DiseasePsychiatryInternal medicine

Abstract

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Abstract Introduction: A marked shift in ambulatory patient assessment was instituted by cancer centers in response to the COVID-19 crisis. At our center, non-urgent appointments were deferred, and essential assessments were conducted virtually whenever possible. Prior to COVID-19, all patients attending ambulatory clinic completed an Edmonton Symptom Assessment Scale (ESAS) form via touch pad, with assistance as needed by clinic volunteers. Our purpose here was to explore how virtual conduct of clinics impacted the collection of patient-reported outcomes and to address the unmet need for recognition and management of severe symptoms, particularly depression/anxiety. Methods: We performed a mixed methods cross-sectional study to test the feasibility of remote completion of the ESAS form by patients scheduled for appointments at a weekly surgical oncology clinic at a major Canadian cancer center. Over the course of the first 5 weeks of the study, patients were phoned after their appointment to request permission to email the ESAS form and asked to return the completed form electronically. Over the next 2 weeks, patients who attended in-person appointments were asked to complete a hard-copy ESAS form in clinic. Clinically significant distress was defined a priori as score >2 for depression and >3 for anxiety. We compared compliance with the two methods (virtual vs. hard-copy) of patient-reported data collection. Results: For the entire study cohort, median age was 64 (35-89) and 48% were female. For the virtual method of ESAS completion, 45 patients had telephone contact attempted: 30 agreed to study participation, 1 declined, and 14 could not be reached despite repeated attempts. For the hard-copy method, all 22 patients approached consented to participation. For the virtual method, 15 patients successfully completed and returned the ESAS form electronically, yielding an overall compliance rate of 33%. For the hard-copy method, the compliance rate was 95% (1 patient deferred after consenting, then did not return the form). There were no differences in patient age, gender, or tumor type between the two methods. For the patients who agreed to the virtual method but did not return a completed electronic form, the following barriers were identified: unable to open/complete PDF; technology phobia; lack of motivation; patient provided invalid email address. Of the completed forms, 28% revealed a depression score >2 and 31% an anxiety score >3; 22% reported both severe depression and anxiety. There was no difference in the degree of distress reported virtually or via hard copy. Conclusions: We have identified significant barriers to the virtual completion of ESAS forms, with a lack of predictive variables. The severe degree of psychological distress reported by over 25% of respondents during the COVID era demonstrates the need for ongoing regular collection and review of these data. Innovative solutions are urgently required to overcome barriers to virtual collection of patient-reported outcomes. Citation Format: Karineh Kazazian, Wendy Johnston, Jessica Bogach, Carol J. Swallow. Barriers to implementation of virtual collection of patient-reported data in the COVID-19 era [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-005.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.160
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.587
GPT teacher head0.658
Teacher spread0.072 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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