Evidence map›Paper›PMID 42627601›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Whose Threshold Did You Inherit? The Apprenticeship of Cancer Referral.

Waseem Jerjes, See Chai Carol Chan, Austen El-Osta, Azeem Majeed

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Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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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

4 authors.

Waseem JerjesSchool of Public Health, Faculty of Medicine, Imperial College London, London, UK. waseem.jerjes@nhs.net.ORCID http://orcid.org/0000-0002-6914-7401
See Chai Carol ChanSchool of Public Health, Faculty of Medicine, Imperial College London, London, UK.
Austen El-OstaSchool of Public Health, Faculty of Medicine, Imperial College London, London, UK.
Azeem MajeedSchool of Public Health, Faculty of Medicine, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer referral thresholds are usually presented as products of guidelines, risk estimates and clinical findings. In practice, trainees also learn them through apprenticeship. A supervisor who refers early, watches cautiously, distrusts certain tests or worries about overinvestigation can shape a learner's future decisions long after the placement ends. This article introduces threshold inheritance in the context of the largely unexamined transmission of cancer referral habits, risk tolerance and explanatory language from supervisor to trainee. The problem is not variation itself. Reasonable clinicians may reach different decisions when evidence is incomplete. The educational risk arises when variation remains tacit, authority substitutes for reasoning, and a local habit is absorbed as a universal standard. Cancer education should therefore make referral thresholds visible and discussable. Supervisors can compare decisions on the same borderline cases, identify which elements arise from evidence, experience, system pressure or patient context, and revisit outcomes without allowing diagnosis to retrospectively determine whether the original decision was sound. Learners should be assessed on calibrated reasoning, safety-netting and their ability to explain what would change the plan, rather than conformity with one trainer's preference. Deliberate threshold calibration could help future clinicians develop an accountable referral practice that is neither inherited uncritically nor defended as instinct.

Indexed as

Cancer referralClinical supervisionDiagnostic uncertaintyMedical educationPrimary careReferral threshold

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Registered trials

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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.