ArticleBreast (Edinburgh, Scotland)2026
External validation of a data-driven algorithm to identify breast cancer recurrences from administrative healthcare data.
Article in Breast (Edinburgh, Scotland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPopulation-based data on the incidence of breast cancer (BC) recurrence are scarce, because tracking the relevant information is resource-consuming. We report the external validation of a data-driven case-finding algorithm to identify selected treatment and procedure codes relating to BC recurrences from administrative healthcare data.
methodsWe linked a cancer registry-based cohort of BC patients from the Emilia-Romagna (northern Italy) regional hospital discharge and outpatient care databases, and we ran the algorithm. We compared the algorithm-determined incidence and temporal pattern of recurrences during 10 years of follow-up with those obtained with a manual clinical chart review.
resultsThe algorithm had a 92.3% (95% confidence interval: 87.2%; 95.8%) sensitivity, a 98.0% (97.0%; 98.8%) specificity, a 88.1% (82.4%; 92.5%) positive predictive value and a 98.8% (97.9%; 99.3%) negative predictive value. The two curves of subhazard rates of total recurrences showed a good degree of overlapping and both exhibited the expected bimodal temporal pattern, with a peak between the 2nd and the 3rd year of follow-up and another between the 8th and the 9th year. The algorithm-based 10-year cumulative incidence function of recurrence was 15.8% (13.7%; 18.1%) versus 15.0% (13.0%; 17.3%). The algorithm captured with good precision the temporal pattern of recurrence by molecular profile. The design of the algorithm excluded the recurrences (11.1% of the total) detected during the first 12 months (HER2-negative BC) and 24 months (HER2-positive BC) after the diagnosis.
conclusionsThis validation study demonstrates that identifying breast cancer recurrences from administrative healthcare data is feasible and sufficiently reliable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.