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Record W4225004757 · doi:10.1212/wnl.0000000000200163

Multiple Sclerosis and the Cancer Diagnosis

2022· article· en· W4225004757 on OpenAlexaffabout
Patti A. Groome, Colleen Webber, Colleen J. Maxwell, Chad McClintock, Dallas Seitz, Alyson Mahar, Ruth Ann Marrie

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of CalgaryUniversity of ManitobaOttawa HospitalManitoba HealthUniversity of WaterlooOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineBreast cancerColorectal cancerCancerInternal medicinePopulationCancer registryOncologyStage (stratigraphy)

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The multiple sclerosis (MS) population's survival from breast cancer and colorectal cancer is compromised. Cancer screening and timely diagnoses affect cancer survival and have not been studied in the MS cancer population. We investigated whether the diagnostic route, cancer stage, or diagnostic interval differed in patients with cancer with and without MS. METHODS: We conducted a matched population-based cross-sectional study of breast cancers (2007-2015) and colorectal cancers (2009-2012) in patients with MS from Ontario, Canada, using administrative data. Exclusion criteria included second or concurrent primary cancers, no health care coverage, and, for the patients without MS, those with any demyelinating disease. We based 1:4 matching of MS to non-MS on birth year, sex (colorectal only), postal code, and cancer diagnosis year (breast only). Cancer outcomes were diagnostic route (screen-detected vs symptomatic), stage (stage I vs all others), and diagnostic interval (time from first presentation to diagnosis). Multivariable regression analyses controlled for age, sex (colorectal only), diagnosis year, income quintile, urban/rural residence, and comorbidity. RESULTS: We included 351 patients with MS and breast cancer, 1,404 matched patients with breast cancer without MS, 54 patients with MS and colorectal cancer, and 216 matched patients with colorectal cancer without MS. MS was associated with fewer screen-detected cancers in breast (odds ratio [OR] 0.68 [95% CI 0.52, 0.88]) and possibly colorectal (0.52 [0.21, 1.28]) cancer. MS was not associated with differences in breast cancer stage at diagnosis (stage I cancer, OR 0.81 [0.64, 1.04]). MS was associated with greater odds of stage I colorectal cancer (OR 2.11 [1.03, 4.30]). The median length of the diagnostic interval did not vary between people with and without MS in either the breast or colorectal cancer cohorts. Controlling for disability status attenuated some findings. DISCUSSION: Breast cancers were less likely to be detected through screening and colorectal cancer more likely to be detected at early stage in people with MS than without MS. MS-related disability may prevent people from getting mammograms and colonoscopies. Understanding the pathways to earlier detection in both cancers is critical to developing and planning interventions to ameliorate outcomes for people with MS and cancer.

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)0.006
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 score0.129
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.077
GPT teacher head0.309
Teacher spread0.232 · 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".

Quick stats

Citations14
Published2022
Admission routes2
Has abstractyes

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