Evidence map›Paper›PMID 42707208›Full record

ArticleFrontiers in public health2026

Reframing the starting point of shared decision-making under algorithmic short-form video exposure: bone tumor care as a sentinel context.

Jia-Wen Wang, Ying-Fa Feng, Zi-Bo Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jia-Wen WangDepartment of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ying-Fa FengDepartment of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Zi-Bo ZhangDepartment of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Short-form video platforms have become a major component of the pre-consultation information environment for patients with cancer in China and may create new challenges for clinical communication. Contemporary shared decision-making (SDM) and patient decision aids (PtDAs) do not explicitly assess or calibrate beliefs formed before consultation. Algorithmic recommendation and emotionally charged narratives may therefore contribute to pre-consultation cognitive anchoring and alter the conditions under which SDM occurs. We use bone tumor care as a sentinel context rather than claiming that these patients are uniquely susceptible to algorithmic influence. Patients with primary bone tumors or bone metastases may receive immune checkpoint inhibitors (ICIs), and some patients with bone metastases also receive bone-modifying agents. Because prevention of medication-related osteonecrosis of the jaw (MRONJ) is most effective before bone-modifying therapy begins, cognition at the initial consultation may be particularly consequential. We hypothesize that pre-consultation anchoring could contribute to delayed reporting of immune-related adverse events (irAEs), gaps in MRONJ prevention, reduced ICI adherence, and more 90-day unplanned visits. The cited studies establish the clinical importance and recognized determinants of these outcomes but do not demonstrate that short-form video exposure causes them. To support future testing, we propose the Digital Exposure-Cognition-Anchoring assessment (DECA) framework: DECA-E records pre-consultation exposure; DECA-C screens and calibrates cognitive anchors; and DECA-L prospectively evaluates longitudinal associations across exposure, cognition, SDM, and clinical safety. DECA is an unvalidated proposal that requires feasibility and outcome evaluation.

Indexed as

AlgorithmsBone NeoplasmsDecision Making, SharedVideo RecordingBisphosphonate-Associated Osteonecrosis of the JawDecision Support TechniquesFemaleHumansImmune Checkpoint InhibitorsMaleImmune Checkpoint Inhibitorsbone metastasiscognitive anchoringhealth misinformationimmune checkpoint inhibitorsmedication-related osteonecrosis of the jawshared decision-makingshort-form video exposure

Identifiers

PMID42707208
PMCPMC13546982

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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.