Evidence map›Paper›PMID 32823314›Full record

ArticleYearbook of medical informatics2020

Untangling Data in Precision Oncology - A Model for Chronic Diseases?

Xosé M Fernández

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

1 author.

Xosé M FernándezInstitut Curie, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAny attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies.

methodsFrom nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don't have judgment, they lack context.

resultsEHRs have become the latest addition to our toolset to look after patients. Moore's law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options.

conclusionsPrecision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas.

Indexed as

Decision Making, Computer-AssistedElectronic Health RecordsGenomicsPrecision MedicineChronic DiseaseHumansMedical OncologyNeoplasms

Identifiers

PMID32823314
PMCPMC7442532

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.