Evidence map›Paper›PMID 23630218›Full record

ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2013

Harnessing technology to improve clinical trials: study of real-time informatics to collect data, toxicities, image response assessments, and patient-reported outcomes in a phase II clinical trial.

M Catherine Pietanza, Ethan M Basch, Alex Lash, Lawrence H Schwartz, Michelle S Ginsberg, Binsheng Zhao, Marwan Shouery, Mary Shaw, Lauren J Rogak, Manda Wilson and 11 more

Abstract read
In one paragraph

Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Trial
  2. Phase II Study of a Non-Platinum-Containing Doublet of Paclitaxel and Pemetrexed with Bevacizumab as Initial Therapy for Patients with Advanced Lung Adenocarcinomas.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2016
    Trial
  3. Digital symptom monitoring in cancer care: an exploratory evaluation of low uptake and engagement.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Optimal assessment of quality of life for patients with prostate cancer.Therapeutic advances in medical oncology · 2022
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Usability of an online application for reporting the burden of side effects in cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2019
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

M Catherine PietanzaThoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. pietanzm@mskcc.org
Ethan M Basch
Alex Lash
Lawrence H Schwartz
Michelle S Ginsberg
Binsheng Zhao
Marwan Shouery
Mary Shaw
Lauren J Rogak
Manda Wilson
Aaron Gabow
Marcia Latif
Kai-Hsiung Lin
Qinfei Wu
Samantha L Kass
Claire P Miller
Leslie Tyson
Dyana K Sumner
Alison Berkowitz-Hergianto
Camelia S Sima
Mark G Kris

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

purposeIn clinical trials, traditional monitoring methods, paper documentation, and outdated collection systems lead to inaccuracies of study information and inefficiencies in the process. Integrated electronic systems offer an opportunity to collect data in real time. PATIENTS AND

methodsWe created a computer software system to collect 13 patient-reported symptomatic adverse events and patient-reported Karnofsky performance status, semi-automated RECIST measurements, and laboratory data, and we made this information available to investigators in real time at the point of care during a phase II lung cancer trial. We assessed data completeness within 48 hours of each visit. Clinician satisfaction was measured.

resultsForty-four patients were enrolled, for 721 total visits. At each visit, patient-reported outcomes (PROs) reflecting toxicity and disease-related symptoms were completed using a dedicated wireless laptop. All PROs were distributed in batch throughout the system within 24 hours of the visit, and abnormal laboratory data were available for review within a median of 6 hours from the time of sample collection. Manual attribution of laboratory toxicities took a median of 1 day from the time they were accessible online. Semi-automated RECIST measurements were available to clinicians online within a median of 2 days from the time of imaging. All clinicians and 88% of data managers felt there was greater accuracy using this system.

conclusionExisting data management systems can be harnessed to enable real-time collection and review of clinical information during trials. This approach facilitates reporting of information closer to the time of events, and improves efficiency, and the ability to make earlier clinical decisions.

Indexed as

Clinical Trials, Phase II as TopicSoftwareAdverse Drug Reaction Reporting SystemsHumansKarnofsky Performance StatusLung NeoplasmsMedical InformaticsPatientsSelf ReportSurveys and QuestionnairesTreatment Outcome

Identifiers

PMID23630218
PMCPMC4878068

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.