Evidence map›Paper›PMID 32953537›Full record

ArticleTranslational lung cancer research2020

Optimizing lung cancer MDT data for maximum clinical impact-a scoping literature review.

Emily Stone, Nicole Rankin, David Currow, Kwun M Fong, Jane L Phillips, Tim Shaw

Abstract readScoping Review
In one paragraph

Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Navigating patient journey in early diagnosis of lung cancer in India.Lung India : official organ of Indian Chest Society
    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

6 authors.

Emily StoneDepartment of Thoracic Medicine, St Vincent's Hospital Sydney, Kinghorn Cancer Centre, University of Sydney, Sydney, New South Wales, Australia.
Nicole RankinResearch in Implementation Science and e-Health (RISe), Faculty of Health Sciences, University of Sydney, Sydney, New South Wales, Australia.
David CurrowIMPACCT, University of Technology Sydney, Ultimo, New South Wales, Australia.
Kwun M FongUQ Thoracic Research Centre and The Prince Charles Hospital, Metro North Hospital and Health Service, Brisbane, QLD, Australia.
Jane L PhillipsIMPACCT, University of Technology Sydney, Ultimo, New South Wales, Australia.
Tim ShawDirector of Research in Implementation Science and eHealth Group (RISe), Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multidisciplinary care in is widely recommended as best practice for lung cancer in many countries and jurisdictions. A number of studies suggest multidisciplinary care benefits patient outcomes, with analyses based on a range of data sources including national, state and local registries as well as multidisciplinary team meeting (MDT)-based data collections, often focused on different questions depending on data sources. MDT data collection and linkage are not standardized and not routine although data collection and feedback are specifically recommended by at least one statutory body. We performed a scoping review of current evidence for lung cancer MDT data collection and analysis, to identify discrete strategies through illustrative examples and to make recommendations for future approaches. Thirteen studies were identified that presented lung cancer MDT-related clinical outcomes, three included MDTs from multiple tumour streams while 10 studies focussed on lung cancer MDT meetings. Eleven studies measured the effect of MDT discussion on clinical outcomes of which eight were positive. Data sources included MDT records (3 studies), medical or hospital records (3 studies), institutional registries (5 studies) and state or national administrative datasets (6 studies), with some overlap. Examples of studies based on different data sources (local MDT, institutional registry, national registry) exemplified the different types of clinical research questions appropriate for each data source. While MDT data collection is not well-defined, the importance of clinical audit and data feedback and the potential for real-time analysis to improve outcomes deserve further investigation. Optimized datasets and linkage strategies are likely to maximize benefits for patients.

Indexed as

clinical datadatabaselung cancermultidisciplinaryMultidisciplinary team (MDT)tumor board

Identifiers

PMID32953537
PMCPMC7481624

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.