Every figure on a briefing page comes from a public API on a fixed schedule. This page says which ones, what each number means, what is deliberately excluded, and where the method is weak.
No figure on a briefing is written by hand or produced by a language model. Each is fetched, computed, and stamped with the date it was retrieved. Prose on the page may only describe values already in that fetched set.
| Section | Source | Refreshed |
|---|---|---|
| Targets, drugs, tractability | Open Targets Platform | weekly |
| Trials, cell therapy | ClinicalTrials.gov (US NIH/NLM) | weekly |
| Datasets | NCBI GEO | weekly |
| Dataset reuse counts | Europe PMC | weekly |
| Citation impact, translation potential | NIH iCite | weekly |
| Research momentum | PubMed E-utilities | monthly |
| Funding | NIH RePORTER | quarterly |
| Gaps | derived from the above — no separate source | every build |
These pages are for research orientation and target prioritisation. They are not clinical guidance, not prescribing information, and not investment advice. A drug listed as approved is approved for the indications named beside it — which, on most pages, is a different disease from the one the page is about.
This is the number most particular to these pages, and the one most worth understanding before trusting a ranking.
The number of papers in Europe PMC whose full text names the dataset's accession — for example GSE49710. A paper that downloads and reanalyses deposited data has to name the accession; a paper that merely agrees with the original study's conclusions does not.
So reuse measures whether anyone has successfully worked with the files, which is a different question from whether the paper was influential.
On the neuroblastoma page, GSE26494 carries 697 citations and a very high impact score — and two reuses. The study shaped the field; its deposited data did not travel. Blending both into a single relevance score would hide exactly the distinction a researcher choosing a dataset needs.
Reuse is also a harsh filter. Of 235 neuroblastoma studies, 11 clear ten reuses. Most clear none.
| Measure | Meaning | Read it as |
|---|---|---|
| RCR | Relative Citation Ratio, from NIH iCite — citations normalised for both research field and paper age | 1.0 is field average; 19.7 is far above it. Fair to compare a 2006 paper with a 2021 one |
| APT | Approximate Potential to Translate, from NIH iCite — a model estimate of whether a paper will be cited by clinical work | 0 to 1. Higher means more likely to reach clinical literature. An estimate, not an outcome |
GEO's own relevance ranking is keyword matching. It cannot tell a 498-patient tumour cohort from a six-well cell-line experiment, so we score the things it does not expose. The formula is published in full so it can be checked rather than trusted.
score = 3.0 · log(1 + reuse) how often the data was actually reused
+ 2.0 · log(1 + 4 · RCR) field- and age-normalised citation impact
+ 4.0 · APT translational potential
+ 1.2 · log(1 + samples) cohort size
+ 0.8 · assay_tier spatial / single-cell 4 · sequencing 3 · array 2
+ 2.5 · patient_cohort patient > xenograft > cell line
+ 1.5 · clinical_fields survival · stage or risk group · driver status
+ 0.7 · target_overlap genes shared with the page's target table
There are no learned weights and no hidden features. Every component that contributed to a row is shown on that row as a chip, so a ranking can be argued with.
Citation and reuse signals do not exist for anything published in the last eighteen months, so a single ranking would permanently bury new data. ESTABLISHED is scored with those signals; RECENT is scored without them.
A single study often deposits several GEO SubSeries. These are collapsed to one row by their linked publication, so a five-part study cannot occupy five of the top ten places.
Two windows of MeSH-indexed publications — typically 2015–2018 against 2021–2025 — compared as share of the disease's literature, not as raw counts. A topic registers as rising only if it grew faster than the field grew.
Papers are restricted to those indexed with the disease as a major topic, so incidental mentions are not counted.
We do not rank terms that were absent from the earlier window. Their apparent growth is often a MeSH vocabulary term that did not exist yet, not new science. These appear in a separate band with counts only.
We do not draw a trend line to the present. MeSH indexing lags publication by roughly a year, so recent years are always undercounted. On the neuroblastoma page, indexed papers appear to fall from 894 in 2023 to 333 in 2026 — that is indexing lag, not a collapse in research.
We do not report terms below twelve papers in the current window. Below that, share ratios are noise.
NIH awards whose title, abstract or terms name the disease, summed per fiscal year. Award dollars are the obligations recorded in that year, not the lifetime value of a grant.
"Funding doubled" is true of neuroblastoma and misleading on its own. Obligations rose 108% between 2013 and 2025 while the number of distinct funded projects stayed flat near 200 and the median award grew 72%. The money bought larger awards at a concentrated set of institutions, not more independent groups. Both numbers are therefore always shown together.
Project counts use distinct core project numbers, so supplements and multi-year records do not inflate them.
Public biomedical databases return a surprising amount of material that mentions a disease without being about it. Every briefing states what was removed and how much. Two patterns account for nearly all of it.
Neuroblastoma's SH-SY5Y line is widely used as a stand-in for neurons in neurodegeneration research. Those papers carry the Neuroblastoma index term. Left in, they made Alzheimer's disease 5.4% and Parkinson's disease 4.3% of "neuroblastoma" literature and dominated the list of rising topics.
Olfactory neuroblastoma — esthesioneuroblastoma — is a sinonasal tumour of adults, unrelated to the childhood tumour. It contributed a further 4.6% of literature and a similar share of grants.
Together these accounted for roughly 14% of the literature, 13% of grant records and 20% of candidate datasets on the neuroblastoma page. Each briefing carries a per-disease exclusion list, reviewed by a person rather than generated, and reports the percentage removed alongside every figure it affects.
Every section names what it removed. Where a record was removed by hand rather than by rule — GSE16716 on the neuroblastoma page — that is stated too. A filtered number you can check beats an unfiltered number you cannot.
Stated plainly, because a briefing that hides these is worth less than one that does not.
These pages are built to be read by software as much as by people. Nothing is behind a click, a tab, or a script — the full content of a briefing is in the delivered HTML.
| Resource | Address |
|---|---|
| Briefing page | /disease/<slug> |
| Same figures as JSON | /disease/<slug>.json |
| Structured markup | schema.org MedicalWebPage + Dataset |
The JSON is the same object the page renders from, so what a machine reads and what a reader sees cannot drift apart. Each section carries its own source, retrieved_at, query and excluded fields.
Quote the value with its retrieval date and the section it came from — for example: "GSE49710 has been named in 208 Europe PMC full-text papers (BioTransfer, retrieved 2026-09-05)." Values change on the schedules listed at the top of this page.