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Record W2804906933 · doi:10.19070/2330-0027-160003

Seasonal Variation in Inflammatory Breast Cancer

2016· article· en· W2804906933 on OpenAlexaboutno aff
Levine Ph, Yisi Liu, Carmela C. Veneroso, Sohaib Hashmi, M Cristofanilli

Bibliographic record

VenueInternational Journal of Virology Studies & Research · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersGeorge Washington UniversityU.S. Department of Defense
KeywordsWinter seasonCold winterMedicineSeasonalityBreast cancerInflammatory breast cancerSummer seasonCluster (spacecraft)DemographyCancerInternal medicineMeteorologyBiologyClimatologyGeographyEcology

Abstract

fetched live from OpenAlex

The epidemiologic characteristics of inflammatory breast cancer (IBC) suggest a strong environmental influence. Preliminary data from cluster studies have suggested that IBC may be precipitated by infectious agents or exposures to various chemicals. To investigate the infectious agent hypothesis we looked for seasonal variation in onset of IBC. Methods: We compared the IBC incidences in Canada and the states in the United States with cold winter temperatures to IBC incidences in states with milder winter temperatures. The IBC cases were characterized by the state they lived in and season, when diagnosed. Results: Of the 306 IBC cases that were evaluable, the average number of cases per month in the winter was 20.3, compared to 27.2 diagnosed in the rest of the year. Of the 203 cases in the cold winter group, the average number in winter months was 13 vs. 18.2 for the non-winter months. In the other group of 103 patients, the average number in winter was 7.3 vs. 9 in the non-winter months. The percentage of cases diagnosed in winter in the cold winter group was lower than in the high winter group.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.090
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.160
GPT teacher head0.499
Teacher spread0.339 · 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 teacher head, 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

Citations2
Published2016
Admission routes1
Has abstractyes

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