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Record W1982077008 · doi:10.5045/kjh.2012.47.1.1

The premier statistical report of hematologic malignancies in Korea

2012· article· en· W1982077008 on OpenAlexaboutno aff
Hee‐Je Kim

Bibliographic record

VenueThe Korean Journal of Hematology · 2012
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationHematologic malignancyHematologic NeoplasmsDiseaseStatisticsCancerEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Tada! The current issue of the Korean Journal of Hematology includes a very impressive, surprising report describing, for the first time, the statistics of hematologic malignancies in Korea [1]. In fact, until now, no reliable nationwide cancer statistics describing hematologic malignancies have been published in Korea. While the report presented in this current issue may be somewhat incomplete, it represents an excellent database that can be used both domestically and worldwide as an important source of cancer statistics from Korea. Because the database contains all aspects of population-based, disease-specific statistics, along with survival- and death-related data from 1993 to 2008, we can analyze and compare our own data with that of other countries, allowing high-ranked public officials to shape national policies and health strategies in the field of hematologic malignancy in the future. On the other hand, this statistics presented in the current issue could be representative of the estimated incidence and prevalence since we still do not have a reporting system that enables accurate data collection and creation of a nationwide database for specific regions or specific periods of time. The data from 1993 and prior should differ quite dramatically from data for 2008. However, we recognize that the publication of this statistical report provides us with a beginning from which we can establish a reliable health reporting system in Korea. Undoubtedly, this is the first step toward developing more complementary and up-to-date health statistics in this country. Based on an annually increasing trend, we can say for certain that greater than 8,000 new cases of blood cancer patients were diagnosed in 2008 according to data from the Korea National Cancer Incidence Database (KNCIDB) [1, 2]. In addition, survival and mortality data from 1993 to 2008 were obtained from the Korea National Statistics Office (KNSO) [1, 2]. These data were combined, and the current report calculated, for the first time in Korea, the incidence, mortality, prevalence, limited-duration prevalence, and changes in the annual age-standardized cancer incidence rates. Now, Korean medical students can be taught oncology using Korea's own statistics, rather than the statistics of other countries. Therefore, although it is somewhat shameful to admit that this is the first publication of national data on hematologic malignancies, we should consider this a memorable and historical achievement. To establish further reliable statistics on hematologic malignancies, we anticipate that more aggressive efforts will be made to collect data from every regional database in Korea. Based on a recent report published in the Journal Cancer Research and Treatment in 2011 [2], we found that 2,561 new cases of leukemia were diagnosed in Korea in 2008. In contrast to this report, the most recent report published in our journal showed that 2,262 new cases of lymphoid and myeloid leukemia occurred during the same year, according to the same investigators. Moreover, the annual increase in incidence was relatively small (1.2-1.4% for leukemia compared with 3.9% for other hematologic malignancies). An international comparison of the age-standardized incidence rates of hematologic malignancies also revealed that this increase was only about one-half of the increase that occurred in USA and Canada. It may be incomparable to other western countries. This data contradiction must be resolved in near future. The National Cancer Institute's Surveillance, Epidemiology and End Results Program (SEER), published online by SEER Review Facts annually [3], may be a paradigm of cancer statistics observation and reporting that should be considered. The data system used in the SEER program is much more friendly to policy makers, researchers, and even clinicians and patients and introduces some new approaches in the field of hematologic malignancies, including somewhat brief guidelines for chemotherapy, radiation, stem cell transplantation, etc. Furthermore, we could investigate psychosocial sequelae, aspects of socioeconomic understanding in the context of quality of life issues, and different aspects of acute and chronic leukemias in adults and children from these SEER reports. A review of the SEER program suggests that we still need to better subdivide and define age-specific incidence rates for each blood cancer. With regard to preventive medicine, it will be helpful for us to study these diseases, if the data include epidemiology, as shown in SEER approaches. Notwithstanding the above mentioned insufficiencies of our first national blood cancer statistics, we are excited to acknowledge the efforts made by many clinicians and researchers in establishing this first official report in Korea. We will develop many more constructive health policies in this field thanks to the framework published today, and we should be proud of ourselves for establishing this base source for investigating important epidemiologic characteristics, evaluating progress in disease management, and establishing future strategies for all hematologic malignancies. In the era of aging in Korea, cancer incidence is on the rise, and continuous efforts must be made to create more efficient cancer control programs and to make data available, with significant attention to detail and exquisite skills in the periodic statistical analysis of hematologic malignancies. We have just scratched the surface in the proficient development of our own cancer control programs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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