ArticleBMC medical research methodology2019
Modelling reassurances of clinicians with hidden Markov models.
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02599506 (Fear of Cancer Recurrence), which is not on this map. Cited by 2 papers.
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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.
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.
Fear of Cancer Recurrence (FoR) Trajectory During Radiation Treatment and Follow-up Into Survivorship of 100 Breast Cancer patients-a Pilot Study
Who cites it
2 citing papers in PubMed.
- Characterizing heterogeneity in health-related quality of life trajectories in coronary artery disease: a longitudinal latent Markov analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- How are emotional distress and reassurance expressed in medical consultations for people with long-term conditions who were unable to receive curative treatment? A pilot observational study with huntington's disease and prostate cancer.Pilot and feasibility studies · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance.
methodsWe fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement.
resultsWe found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previous reassurance, the more likely the clinician is to stay in the current state.
conclusionsHMMs prove to be a valuable tool and provide important insights for practitioners.
trial registrationTrial Registration number: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.
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