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Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information.

ABSTRACT: Evidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice-especially when it contradicts their own judgement ("egocentric advice discounting")-but this can be mitigated by knowledge about the advisor's past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm's derivation, validation, and accuracy. We measured weight of advice and found that, on average, GPs weighed their estimates and the algorithm equally-but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.

SUBMITTER: Palfi B

PROVIDER: S-EPMC9329504 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDespite their involvement in the regulation of gene expression and their importance as genomic markers for promoter prediction, no objective standard exists for defining CpG islands (CGIs), since all current approaches rely on a large parameter space formed by the thresholds of length, CpG fraction and G+C content.ResultsGiven the higher frequency of CpG dinucleotides at CGIs, as compared to bulk DNA, the distance distributions between neighboring CpGs should differ for bulk and island CpGs. A new algorithm (CpGcluster) is presented, based on the physical distance between neighboring CpGs on the chromosome and able to predict directly clusters of CpGs, while not depending on the subjective criteria mentioned above. By assigning a p-value to each of these clusters, the most statistically significant ones can be predicted as CGIs. CpGcluster was benchmarked against five other CGI finders by using a test sequence set assembled from an experimental CGI library. CpGcluster reached the highest overall accuracy values, while showing the lowest rate of false-positive predictions. Since a minimum-length threshold is not required, CpGcluster can find short but fully functional CGIs usually missed by other algorithms. The CGIs predicted by CpGcluster present the lowest degree of overlap with Alu retrotransposons and, simultaneously, the highest overlap with vertebrate Phylogenetic Conserved Elements (PhastCons). CpGcluster's CGIs overlapping with the Transcription Start Site (TSS) show the highest statistical significance, as compared to the islands in other genome locations, thus qualifying CpGcluster as a valuable tool in discriminating functional CGIs from the remaining islands in the bulk genome.ConclusionCpGcluster uses only integer arithmetic, thus being a fast and computationally efficient algorithm able to predict statistically significant clusters of CpG dinucleotides. Another outstanding feature is that all predicted CGIs start and end with a CpG dinucleotide, which should be appropriate for a genomic feature whose functionality is based precisely on CpG dinucleotides. The only search parameter in CpGcluster is the distance between two consecutive CpGs, in contrast to previous algorithms. Therefore, none of the main statistical properties of CpG islands (neither G+C content, CpG fraction nor length threshold) are needed as search parameters, which may lead to the high specificity and low overlap with spurious Alu elements observed for CpGcluster predictions.

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Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information.

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