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Accuracy of a mobile sensor-based system for the detection of chemotherapy toxicity in older adults with cancer.

2024· article· en· W4399324290 on OpenAlexaff
Enrique Soto‐Pérez‐de‐Celis, Eshetu G. Atenafu, Ana Cristina Torres, Andrea de la O Murillo, Andrea Morales Alfaro, María Reneé Jiménez Sotomayor, Celia Gabriela Hernandez Favela, Javier Monroy Chargoy, José Carlos Aguilar-Velazco, Mildred Medina, María Luisa Moreno-García, Maria de la Concepción Pérez de Celis, Sergio Contreras‐Garduño, Tania Chávez, Paula Cárdenas-Reyes, Shabbir M.H. Alibhai

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersConquer Cancer Foundation
KeywordsMedicineToxicityChemotherapyOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

1515 Background: Older adults receiving chemotherapy (CT) are at a high risk of toxicity (Tox) and of functional decline. Promptly acting upon CT Tox in older adults is difficult, particularly in developing countries where triage systems and personnel are limited. We previously showed that monitoring older patients using an accelerometer-equipped smartphone is feasible, and that a decline in the number of steps/day may represent a marker of Tox. We aimed to evaluate the diagnostic accuracy of an objective patient-centered measure of physical function (steps/day) for the remote detection of Tox in older adults receiving CT. Methods: We included consecutive patients aged ≥65 years starting first line CT for solid tumors at a single center in Mexico City. Patients underwent a geriatric assessment and were provided with an accelerometer-equipped smartphone. Daily steps were recorded for ≥7 days pre-CT initiation, and median number of pre-CT steps/day was calculated. Patients with ≤600 median pre-CT steps/day, and those using walking aids, were excluded (16% of recruited patients). Steps were monitored daily, compared with median pre-CT steps/day, and % decline was calculated. Patients were called daily, and Tox was assessed using PRO-CTCAE questionnaires. The % decline in steps/day from pre-CT was considered as the index test for Tox, while patient report was considered the reference standard. The association between % step decline and moderate/severe Tox was examined using generalized linear-mixed models. AUC was calculated and Youden’s index used to choose cutoff points for Tox detection. Results: 116 patients were included (96 development cohort, 20 validation cohort). Median age was 73y (range 65-91), 55% were female, and 65% had ≤high school education. The most common tumors were colon (21.5%), pancreas (17.5%), and gastric (12%). 28% of participants had never used a smartphone. The median number of pre-CT steps/day was 2979. Patients were followed for 6764 days, with Tox detected on 64.4% of days. Moderate/severe self-reported toxicity was detected on 1245 days (22.1%), while mild/no toxicity was detected on 4378 days (77.9%). AUC analysis for the development cohort demonstrated that a 32% decrease in steps/day from pre-CT median showed a sensitivity of 77.6% and a specificity of 67.3% for detecting moderate/severe Tox. Sensitivity and specificity of the cutoff in the validation cohort were 75.8% and 69.6%, respectively. Tox associated with a decline in steps/day included fatigue, pain, and nausea. Conclusions: A decline in the number of steps/day measured using an accelerometer-equipped smartphone was useful for the remote detection of moderate/severe Tox in older adults with cancer receiving CT, with a high sensitivity and specificity. This patient-centric measure could be used in clinical practice and research to detect and act promptly on Tox and, potentially, improve outcomes.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.085
GPT teacher head0.509
Teacher spread0.424 · 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 designOther design
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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