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A mixed methods study exploring the role of perceived side effects on treatment decision-making in older adults with acute myeloid leukemia (AML).

2021· article· en· W3169033212 on OpenAlexaffabout
Dawn Maze, Roland B. Walter, Diana M. Merino, Timothy Bell, Louise O’Hara, François Péloquin, Lucy Morgan, Loriana Hernandez, Jasmine Healy, Charlotte Panter, Verna Welch, Adam Gater, Karim Amer, Ryan Hohman, Julia Stein, Andrew Brown, Nigel H. Russell, Thomas W. LeBlanc

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsPfizer (Canada)Princess Margaret Cancer Centre
FundersPfizer
KeywordsMedicineMyeloid leukemiaInternal medicineChemotherapyFamily medicineIntensive care medicine

Abstract

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7016 Background: AML patients may be treated with intensive chemotherapy (IC), or non-intensive chemotherapy (NIC) or they may receive best supportive care (BSC) or hospice care. Balancing treatment efficacy and toxicities is key in treatment decision-making. IC is efficacious with extensive toxicities, while NIC has lower risk of toxicities but reduced efficacy. This study provides an international, multi-stakeholder perspective on the role of side effects in AML treatment decision-making. Methods: We conducted one-on-one, 60-minute interviews with 28 AML patients (>65 years, not receiving IC), 25 of their family members and 10 independent physicians from the US, UK and Canada. Interviews included open-ended questions to explore the treatment decision-making process. Participants also rated the importance of various factors in AML treatment decision-making from 0 (not important) to 3 (very important). Results: The sample included patients with varying treatment histories (13 no treatment, 11 on NIC, 3 discontinued NIC, 1 BSC). Side effects were rated as a ‘very important’ factor in treatment decision-making by a greater proportion of patients not on treatment (n = 9/13; 69.2%) and their relatives (n = 12/13; 92.3%) compared to those with experience of NIC (n = 5/11 who answered, 45.5%), their relatives (n = 3/11; 27.3%), and physicians (n = 4/10; 40.0%). When discussing side effects in detail, there was a disconnect between perceptions of patients not on treatment, and side effects that patients on NIC actually experienced. Many patients with no treatment experience were worried that side effects would be worse than their current symptoms (n = 6/13), referring to constant vomiting, hair loss, organ failure, or death. Fear of side effects was the primary reason for opting not to take treatment (n = 9/13), though it was not clear if these patients were distinguishing between IC and NIC. In contrast, although two patients’ experiences of side effects resulted in them discontinuing NIC (n = 2/14), a higher proportion (n = 9/14) reported that the side effects had little impact on their life. Side effects most frequently reported by patients with experience of NIC (n = 11/14) were considered mild and included fatigue, reduced appetite, generally feeling unwell, nausea and injection site irritation (all n = 3). It was most commonly reported that the worst aspect of NIC was the time commitment (n = 4/8 asked). When accounting for different treatments paths no international variation in findings was observed. Conclusions: The nature and severity of side effects of AML treatment were perceived to be worse than reality. This incorrect perception may lead to undertreatment of patients and result in worse outcomes. There is a need for more patient education and resources about the lived treatment experience, to enhance understanding and mitigate pre-conceived notions of side effects.

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)0.018
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.029
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.094
GPT teacher head0.500
Teacher spread0.406 · 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 designQualitative
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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Citations3
Published2021
Admission routes2
Has abstractyes

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