Evidence map›Paper›PMID 28684551›Full record

ArticleThe oncologist2017

Transitions in Prognostic Awareness Among Terminally Ill Cancer Patients in Their Last 6 Months of Life Examined by Multi-State Markov Modeling.

Chen Hsiu Chen, Fur-Hsing Wen, Ming-Mo Hou, Chia-Hsun Hsieh, Wen-Chi Chou, Jen-Shi Chen, Wen-Cheng Chang, Siew Tzuh Tang

Open access · bronzeAbstract read
In one paragraph

Article in The oncologist, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Associations of prognostic-awareness-transition patterns with end-of-life care in cancer patients' last month.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022
    Article
  7. Article
  8. Article
  9. Review
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

8 authors at 3 institutions in 1 country.

Chen Hsiu ChenDepartment of Nursing, University of Kang Ning, Taipei and Graduate Institute of Clinical Medical Science, Chang Gung University, Tao-Yuan, Taiwan.
Fur-Hsing WenDepartment of International Business, Soochow University, Taipei, Taiwan.
Ming-Mo HouDivision of Hematology-Oncology, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan, and School of Medicine, Chang Gung University College of Medicine, Tao-Yuan, Taiwan.
Chia-Hsun HsiehDivision of Hematology-Oncology, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan, and School of Medicine, Chang Gung University College of Medicine, Tao-Yuan, Taiwan.
Wen-Chi ChouDivision of Hematology-Oncology, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan, and School of Medicine, Chang Gung University College of Medicine, Tao-Yuan, Taiwan.
Jen-Shi ChenDivision of Hematology-Oncology, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan, and School of Medicine, Chang Gung University College of Medicine, Tao-Yuan, Taiwan.
Wen-Cheng ChangDivision of Hematology-Oncology, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan, and School of Medicine, Chang Gung University College of Medicine, Tao-Yuan, Taiwan.
Siew Tzuh TangDepartment of Nursing, Chang Gung Memorial Hospital at Kaohsiung, and Division of Hematology-Oncology, Chang Gung Memorial Hospital at Linkou sttang@mail.cgu.edu.tw.
Chang Gung Memorial Hospital · TWSoochow University · TWUniversity of Kang Ning · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeveloping accurate prognostic awareness, a cornerstone of preference-based end-of-life (EOL) care decision-making, is a dynamic process involving more prognostic-awareness states than knowing or not knowing. Understanding the transition probabilities and time spent in each prognostic-awareness state can help clinicians identify trigger points for facilitating transitions toward accurate prognostic awareness. We examined transition probabilities in distinct prognostic-awareness states between consecutive time points in 247 cancer patients' last 6 months and estimated the time spent in each state.

methodsPrognostic awareness was categorized into four states: (a) unknown and not wanting to know, state 1; (b) unknown but wanting to know, state 2; (c) inaccurate awareness, state 3; and (d) accurate awareness, state 4. Transitional probabilities were examined by multistate Markov modeling.

resultsInitially, 59.5% of patients had accurate prognostic awareness, whereas the probabilities of being in states 1-3 were 8.1%, 17.4%, and 15.0%, respectively. Patients' prognostic awareness generally remained unchanged (probabilities of remaining in the same state: 45.5%-92.9%). If prognostic awareness changed, it tended to shift toward higher prognostic-awareness states (probabilities of shifting to state 4 were 23.2%-36.6% for patients initially in states 1-3, followed by probabilities of shifting to state 3 for those in states 1 and 2 [9.8%-10.1%]). Patients were estimated to spend 1.29, 0.42, 0.68, and 3.61 months in states 1-4, respectively, in their last 6 months.

conclusionTerminally ill cancer patients' prognostic awareness generally remained unchanged, with a tendency to become more aware of their prognosis. Health care professionals should facilitate patients' transitions toward accurate prognostic awareness in a timely manner to promote preference-based EOL decisions. IMPLICATIONS FOR PRACTICE: Terminally ill Taiwanese cancer patients' prognostic awareness generally remained stable, with a tendency toward developing higher states of awareness. Health care professionals should appropriately assess patients' readiness for prognostic information and respect patients' reluctance to confront their poor prognosis if they are not ready to know, but sensitively coach them to cultivate their accurate prognostic awareness, provide desired and understandable prognostic information for those who are ready to know, and give direct and honest prognostic information to clarify any misunderstandings for those with inaccurate awareness, thus ensuring that they develop accurate and realistic prognostic knowledge in time to make end-of-life care decisions.

Indexed as

Decision MakingAgedAwarenessFemaleHumansLongitudinal StudiesMaleMarkov ChainsMiddle AgedNeoplasmsPatient PreferenceProfessional-Patient RelationsPrognosisQuality of LifeTaiwanTerminal CareAwarenessMulti‐state modelNeoplasmsPrognosisPrognostic awarenessTransitions

Identifiers

PMID28684551
PMCPMC5599205
OpenAlexW2727475191

What OpenQuestion holds

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Registered trials

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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.