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Current state content analysis of patient-reported outcome measures and clinical needs assessment tools utilized throughout the cancer care continuum at an academic medical center.

2024· article· en· W4402987007 on OpenAlexaboutno aff
Alayna E. Ernster, Beth A. Fisher, Sarah A. Birken

Bibliographic record

VenueJCO Oncology Practice · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsCenter (category theory)CancerMedicineState (computer science)Medical physicsFamily medicineMedical educationGerontologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

238 Background: The Cancer Survivorship Care Quality Framework (CSCQ-F) is an evidence-based framework that outlines domains and indicators pertinent to the provision of quality cancer survivorship care. Many indicators from the CSCQ-F can be assessed by clinical interview or patient-reported outcome measures (PROMs) to gauge whether patients are receiving quality survivorship care. We conducted a current-state analysis of the PROMs and needs assessment tools (NATs) utilized throughout our academic medical center and assessed the extent to which constructs covered on our assessments align with relevant domains and indicators from the CSCQ-F. Methods: Healthcare leaders identified assessments currently available for use throughout oncology clinics at our institution. We included assessments directly completed by patients (i.e., PROMs) and assessments completed by clinicians based on patient feedback gathered through clinical interview or oncology nurse navigation (i.e., NATs). We excluded assessments not currently in use. We coded each assessment item by construct (e.g., depression, neuropathy, substance use, referral) and time of administration (e.g., new patient visits, treatment change, unknown). Then, we mapped items/constructs to the CSCQ-F domains. Afterward, we compared items/constructs mapped to the CSCQ-F domains with CSCQ-F indicators to identify gaps in assessment. Results: We identified two PROMs (Lucet and The Edmonton Symptom Assessment Scale (ESAS)) and two NATs (Epic Wheel and an internally developed cancer navigation assessment). Constructs covered by these assessments aligned with all domains of the CSCQ-F, except for “Patient/Caregiver Experience.” Gaps in assessment based on the CSCQ-F depend on which assessments patients receive. All new patients are administered Lucet (an electronic distress screening tool) and referred for cancer navigation assessment. However, ESAS is administered through Palliative Care, and it is unclear whether and when all providers administer Epic Wheel (a social determinants of health NAT). The CSCQ-F indicates reassessment of symptoms and/or conditions should occur at defined intervals throughout the cancer continuum, but there is no clear reassessment protocol at our institution. Conclusions: To our knowledge, this is the first content-analysis of PROMs and NATs mapped to the CSCQ-F. Our results signify a need to (1) address gaps in assessment, (2) determine a protocol for symptom reassessment, and (3) standardize enterprise-wide administration procedures for PROMs and NATs throughout the cancer continuum. Future work should also identify who is responsible for following up on patients’ identified concerns and determine whether adequate resources are available to address patients’ symptoms and needs.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.268
GPT teacher head0.491
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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