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Survey of challenges in access to diagnostics and treatment for neuroendocrine tumor patients (SCAN): The diagnostic process of GEP-NETs in Australia, Canada, China, France, Germany, the United Kingdom, and the United States of America.

2022· article· en· W4205793481 on OpenAlexaffabout
Mark McDonnell, Catherine Bouvier, Marianne E. Pavel, Harjit Singh, James R. Howe, Simron Singh, Jie Chen, Dirk Van Genechten, Elyse Gellerman, Sugandha Dureja, Simone Leyden, Christine Rodien‐Louw, Teodora Kolarova, Dermot O’Toole

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRespondentNeuroendocrine tumorsInternal medicineFamily medicineDemographyPediatrics

Abstract

fetched live from OpenAlex

502 Background: SCAN assessed the global delivery of NET diagnostics and treatment. This analysis focused on the diagnostic process in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients in countries with more robust respondent samples (at least 100 units of analysis): Australia (AU), Canada (CA), China (CH), France (FR), Germany (DE), United Kingdom (UK), and the United States (USA). Methods: During Sept-Nov 2019, 2359 NET patients & caregivers, and 436 healthcare professionals (HCPs) completed a self-reported online survey, available in 14 languages, disseminated via INCA and its partner networks. Results: 71% (1670/2359) were GEP-NET patients, 71% of which were from 7 countries (1188/1670), namely AU (7%, 120/1670), CA (9%, 154/1670), CH (7%, 114/1670), FR (8%, 137/1670), DE (9%, 149/1670), UK (11%, 191/1670) and USA (19%, 323/1670). Primary GEP-NETs were predominantly small intestinal (SI) with similar proportions in AU, CA, DE, FR,, UK and US and smaller in CH (*p < 0.001, Chi-squared). Second most common primary was pancreatic NET (similar across countries). Misdiagnosis was very frequent and occurred at least once but most commonly multiple times (table). The most frequent misdiagnoses were irritable bowel syndrome (AU 60%, CA 34%, CH 14%, FR 27%, DE 31%, UK 55%, USA 55%) and gastritis (AU 42%, CA 37%, CH 51%, FR 51%, DE 37%, UK 30%, USA 51%). Patients presented with stage IV disease in more than half of cases in 5 countries (table). On average three HCPs were involved in the diagnostic process in all above-mentioned countries. The HCPs who most often suggested the test that led to the correct diagnosis were gastroenterologists in CH 28%, FR 43%, DE 34%, USA 28%, and GPs in AU 28%, CA 27%, and UK 24% (45/191). In the majority of cases the diagnosis was received in a hospital without a NET specialist, except for CH (AU 38%; CA 42%; CH 25%; FR 36%, DE 51%, UK 44%, USA 45%). Conclusions: SCAN demonstrates some interesting geographical variations with respect to tumor demographics and stage at presentation. Nonetheless, delayed GEP-NET diagnosis remains a significant global challenge. Enhanced knowledge about GEP-NETs in hospitals without NET specialists, especially among gastroenterologists and family doctors (GPs), will drive improvements in global NET care.[Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.165
GPT teacher head0.471
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes2
Has abstractyes

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