Evidence map›Paper›PMID 41556259›Full record

ArticleAnnals of the Royal College of Surgeons of England2026

Internal validation protocol for large collaborative clinical data sets: assessment of the CONGRESS database.

K Cole, J A Gossage, P Bhandari, N S Blencowe, S Chidambaram, T Crosby, Rpt Evans, E A Griffiths, S K Kamarajah, S R Markar and 3 more

Abstract readValidation StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of the Royal College of Surgeons of England, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

K ColePortsmouth Hospitals University NHS Trust, UK.
J A GossageGuy's and St Thomas' Hospital NHS Foundation Trust, UK.
P BhandariPortsmouth Hospitals University NHS Trust, UK.
N S BlencoweUniversity of Bristol, UK.
S ChidambaramImperial College London, UK.
T CrosbyVelindre University NHS Trust, UK.
Rpt EvansUniversity Hospitals Birmingham NHS Foundation Trust, UK.
E A GriffithsUniversity Hospitals Birmingham NHS Foundation Trust, UK.
S K KamarajahUniversity Hospitals Birmingham NHS Foundation Trust, UK.
S R MarkarUniversity of Oxford, UK.
N TrudgillUniversity Hospitals Birmingham NHS Foundation Trust, UK.
T J UnderwoodUniversity of Southampton, UK.
P H PucherPortsmouth Hospitals University NHS Trust, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMulticentre clinical research collaboratives collect large, generalisable data sets. However, data are often collected by trainees who may lack clinical or academic experience, raising concerns about data quality and potential reporting bias. Validation practices in such studies are variable. This study outlines the methods, feasibility, and outcomes of internal data validation using the CONGRESS database.

methodsThe multicentre CONGRESS data set of early oesophagogastric cancer was assessed. A random 20% sample of patients was selected to meet a >15% target validation size. Patient, disease and outcome data were re-abstracted from medical records and entered into a validation data set, which was compared with the original database. Cohen's kappa coefficient (κ) and Pearsons corelation (

resultsIn total, 302 patients (18.1%) from the original CONGRESS database were included in the validation data set and 3,320 data points were compared between data sets (6,640 total). The percentage of exact agreement for variables ranged from 82.5% to 98.7% (median 92.3%, interquartile range 86.3%-95.7%). Nine variables (1,645 of 2,946, 55.8% data points) showed 'almost perfect' agreement (κ or

conclusionThis study proposes a reproducible framework and benchmarks for validating large collaborative clinical data sets, using the national CONGRESS data set as an example. This approach offers a standard for ensuring reliable, high-quality research outcomes across multicentre databases.

Indexed as

Databases, FactualDatasets as TopicEsophageal NeoplasmsStomach NeoplasmsCongresses as TopicData AccuracyHumansReproducibility of ResultsCollaborative researchData validationEarly oesophageal cancerOesophagogastric cancer

Identifiers

PMID41556259
PMCPMC13130524

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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.