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Record W2499454114 · doi:10.1158/1538-7445.am2015-3708

Abstract 3708: Filling the void of Canadian T-cell lymphoma epidemiology: Data from the canadian institute for health information discharge abstract database

2015· article· en· W2499454114 on OpenAlexaffabout
Etienne Mahé, Trevor J. Pugh, Tracy Stockley, Suzanne Kamel‐Reid

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsEpidemiologyMedicinePopulationIncidence (geometry)DatabaseDemographyFamily medicineEnvironmental healthPathologyComputer science

Abstract

fetched live from OpenAlex

Abstract Introduction Epidemiological data for rare malignancies can be difficult to obtain; population-based data sets are often abridged to group disparate rare malignancies into larger more manageable clusters. These problems are acute in Canada, where such epidemiological data are often estimated from “second-hand” US data (e.g. Surveillance, Epidemiology and End-Results (SEER) data). Recently, the Canadian Institute of Health Information (CIHI) has set out to make epidemiological data more readily accessible, permitting University-affiliated researchers to access the anonymized and codified CIHI Discharge Abstract Database (DAD). Methods We undertook to estimate the incidence, demographics, and outcome data relating to the various subtypes of peripheral T-cell lymphomas (PTCLs) in Canada (excluding Quebec and British Columbia, for which data was not collected). The CIHI DAD consists of a two fiscal-year anonymized 10% random sample of all hospital discharge abstracts in Canada. The DAD is indexed by a unique anonymous patient identifiers and includes relative date metrics, by which all dates originally present on the abstract are standardized to a unique but confidential CIHI DAD reference date. From these data we were able to identify all hematolymphoid diagnoses, isolate the PTCLs, separate new from historical diagnoses, and identify patient age range, gender and disposition data. When the disposition data were combined with the relative date metrics, a gross estimate of T-cell lymphoma overall survival (relative to all other hematolymphoid diagnoses) was generated. Population normalization was achieved using inter-censal estimates obtained from Statistics Canada. Results PTCL incidence was estimated at 0.72 cases per 100,000 per annum (comparable to recently published SEER data). We also estimated a prevalence of 21 PTCLs per 100,000 healthcare encounters. Most cases of PTCL originated from males (63%) and the distribution of age ranges was skewed toward older adults (median age by number of cases = 60 years). The most frequent diagnosis was PTCL, NOS (46%). By the Cox-proportional hazards method, there was a statistically significant difference in survival between the T-cell lymphomas and non T-cell hematolymphoid malignancies (regression co-efficient for PTCL vs. non-PTCL diagnosis p = 0.003) in favor of the latter; not surprisingly, age was also predictive of overall survival, regardless of the subtype of malignancy (regression co-efficient p = 0.004). Conclusions To our knowledge, the above is the first attempt to estimate the epidemiology of PTCLs in Canada. In addition, we present a unique approach to obtaining high-quality (albeit geographically incomplete) Canadian epidemiological data via the CIHI DAD database; this dataset may serve as a valuable resource in the context of rare diseases whose epidemiological data may not be widely or publicly available. Citation Format: Etienne R. Mahe, Princess Margaret Cancer Centre Advanced MolecularDiagnostics Laboratory, Trevor Pugh, Tracy Stockley, Suzanne Kamel-Reid. Filling the void of Canadian T-cell lymphoma epidemiology: Data from the canadian institute for health information discharge abstract database. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3708. doi:10.1158/1538-7445.AM2015-3708

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)0.002
metaresearch head score (Gemma)0.019
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: none
Teacher disagreement score0.028
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.024
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.283
GPT teacher head0.413
Teacher spread0.130 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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