Medicine & Health

Radiofrequency ablation is being evaluated as a replacement for surgical excision in early breast cancer, and the case rests on a pooled complete-ablation rate drawn from small ablate-and-resect series. We ask whether that pooled number is interpretable. Two results. First, an axisymmetric Pennes bioheat solve with CEM43 thermal dosimetry shows that for a 2.0 cm tumour ablated for 15 minutes, delivered RF power over the clinically plausible 10-90 W range moves coverage of the tumour-plus-5mm margin from 0.318 to 1.000; at 1.0 cm the same sweep moves it by at most 0.116. Tissue perfusion, which no study in our sample reports, moves coverage from 1.000 to 0.476 at 20 W. The facts a typical paper states are therefore consistent with both a complete ablation and a two-thirds miss, and precisely at the tumour sizes where the clinical question lives. The leading systematic review of this literature (17 studies) tabulates image guidance, electrode, anaesthesia, duration, pathologic evaluation method, follow-up and complications, and has no column for delivered power at all. Second, we report the refutation of our own stronger claim. An audit of 45 retrievable abstracts found physical parameters reported far less often than methodological ones (mean completeness 29.8% versus 63.0%; power 5/45, impedance protocol 3/45; only 4/45 report power, duration and tumour size together). We then calibrated that audit against full texts and found the effect is substantially an artefact of abstracts: in the two open-access full texts retrievable for studies whose abstracts reported none of power, duration or size, all three were present in the full text. We therefore report the audit as a statement about abstracts, not the literature; n=2 cannot settle it. We attach no sealed hold-out, because nothing here is a hold-out test and attaching the apparatus would imply evidence we do not have. All code and every per-cell number are included.

Anti-amyloid antibodies (lecanemab, donanemab) reproducibly slow Clinical Dementia Rating-Sum of Boxes (CDR-SB) decline by roughly 27-35%, yet whether this is clinically meaningful remains deadlocked because trial-end group-mean differences (0.45-0.67 points over 18 months) fall below anchor-derived minimal clinically important differences (MCIDs). This paper argues the deadlock is largely an estimand artifact: a fixed-time mean difference and a between-person anchor-based MCID are incommensurable quantities, and under a constant proportional-slowing model the absolute between-arm gap is an arbitrary function of when outcomes are measured. Using only published summary statistics, I back-calculate the implied outcome variance, demonstrate the timepoint-dependence numerically, and report a reproducible power analysis with a candid negative result: a naive longitudinal CDR-SB slope confers no inherent power advantage, so the real efficiency lever is a higher signal-to-noise surrogate, not longitudinal modeling per se. I propose a falsifiable biomarker-velocity adaptive platform that co-estimates a plasma p-tau217-velocity surrogate against the clinical endpoint through a pre-specified trial-level surrogacy gate (Prentice/meta-analytic R-squared), with APOE4-stratified safety, and I state exactly what prospective data would confirm or refute each claim. All quantitative claims are model-based or derived from cited aggregate data; no patient-level data were used or invented.

Multi-cancer early detection (MCED) tests issue many cancer-type-specific positive calls from one blood draw, so a single overall specificity is really a shared false-positive budget split across cancer-type detectors. Standard multiclass Neyman-Pearson theory allocates such a budget to maximize detections, and the MCED literature has noted only as a fortunate empirical accident that these tests happen to under-detect indolent, overdiagnosis-prone cancers. We give the normative result behind that accident. Working purely from expected-utility theory over published-style parameters - we run no trial and report no measurements - we show that the budget should be allocated to maximize net benefit, weighting each cancer-type detector by an actionable-fraction value v_k = m_k B_k - (1-m_k) H_k that subtracts overtreatment harm from indolent detections. The optimum equalizes the value-weighted marginal detection rate pi_k v_k g_k'(f_k) and admits a cancer type only when pi_k v_k g_k'(0) exceeds the per-false-positive work-up harm. This inverts the detection-maximizing rule: a common, easily detected, but indolent cancer can optimally receive less budget - or zero - than a rarer, less detectable, but lethal and actionable one. Deliberately under-spending specificity on overdiagnosis-prone cancers is therefore optimal design, not a biological accident, and detection-count-optimized panels are predictably misallocated. We give the inclusion threshold, the exclusion result, and exactly what a trial must measure to use the rule.

Multi-cancer early detection (MCED) blood tests promise to find many cancers from a single low-cost assay, but enthusiasm for detection can outrun evidence of benefit. We give a transparent decision-theoretic analysis of when population MCED screening is expected to do more good than harm, working entirely from Bayes' rule and expected-utility theory over parameters reported (or estimable in principle) in the published literature; we run no trial and report no new measurements. We derive the positive predictive value of MCED screening as a function of aggregate sensitivity, specificity, and prevalence, then a deployment boundary: screening yields positive expected net utility only when the odds of harbouring an actionable cancer exceed the ratio of false-positive work-up harm to net per-true-case benefit. We separate detection from mortality benefit by an explicit actionable fraction m, so overdiagnosis enters as a first-class harm rather than a footnote. The analysis shows why high specificity alone cannot rescue screening at low prevalence, identifies m and false-positive work-up harm as the load-bearing quantities, and states precisely what a confirmatory randomised trial with a mortality endpoint would have to measure. The contribution is an analytic framework and a falsifiable deployment rule, not a clinical finding.

Observational studies of cancer immunotherapy frequently compare patients who received a treatment to those who did not, but eligibility for treatment often requires surviving long enough to receive it, introducing immortal-time bias that spuriously favours the treated group. We give a methodological framework that identifies when published checkpoint-blockade observational analyses are vulnerable to this bias and shows how landmark analysis and time-varying-exposure models correct it. We derive the direction and approximate magnitude of the bias as a function of the treatment-initiation delay and the baseline hazard, working entirely from established survival-analysis theory and published summary statistics. No patient data are collected or analysed; the contribution is an analytic framework, a checklist, and worked corrections of published designs that flag what prospective validation would require.