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The ABCs of ICD

2015· editorial· en· W2045978941 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Skin & Wound Care · 2015
Typeeditorial
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEpidemiologyPublic healthDiseasePandemicSmallpoxNosologyPlague (disease)Medical emergencyInfectious disease (medical specialty)PsychiatryCoronavirus disease 2019 (COVID-19)Pathology

Abstract

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FigureThe International Statistical Classification of Diseases and Related Health Problems (ICD) is steeped in history, international cooperation, and improvement over the last 150 years, with the most recent revision known as ICD-10 to be implemented by October 1, 2015. The concept of ICD is rooted in the theory of Nosology (the systematic classification of diseases). Nosologic classification began in antiquity, resulting out of the need for nurses, physicians, epidemiologists, and public health entities to classify and make sense of cause of death and morbidity; the parallel use of these data can be traced to the 15th century in Italy, as a result of the “great pandemics of plague.”1 During the Crimean War (1853–1856), Florence Nightingale (1820–1910) applied scientific rigor to explain her observations—“more soldiers died of infection and disease transmission than battlefield wounds—for every 1 soldier that died of his wounds, 7 died of disease.”1 In her attempts to classify morbidity and mortality, she used evidence-based techniques, such as epidemiology, surveillance, and prevention through infection control (hand washing, infection control, and infectious waste management). She also collaborated with William Farr, MD (1807–1883)—a pioneer of epidemiology and statistics. This relationship facilitated her participation in the 1860 International Statistical Congress, where she advocated for “the first model for the systematic collection of hospital data using a uniform classification of diseases and operations that was to form the basis of the ICD code used today.”1–3 In 1891, Jacques Bertillon introduced an alphanumeric method of disease classification,1 which incorporated disease by anatomical site and cause of death. With subsequent revisions, his list became known as “The First Revision (ICD-1).”1 The ICD-1 was principally used in European countries and translated from French to English, Spanish, and German. In 1897, the American Public Health Association (APHA) recommended adoption of the Bertillon classification by all registrars of vital statistics in the United States, Canada, and Mexico. Coincidental to the seventh revision, the World Health Care Organization (WHO) established the “WHO Center for Classification of Diseases.” In 1948, the WHO coordinated and organized “international study groups” and adopted the concept of the ICD and expanded morbidity coding in 1949. In 1977, the WHO published the ninth revision, known as ICD-9, consisting of 3 volumes containing diagnostic and procedure codes.1,5 Although we think of ICD-10 as new, the development of ICD-10 began in 1993 and was released in 1993 in Europe and several other countries, but was not adopted in the United States until October 2014 (implementation date October 1, 2015). The ICD-10 is intended to provide more specificity about disease conditions and healthcare interventions than previous versions. To that end, the alphanumeric designation numeric codes for billing and statistical analysis jump from 14,000 in ICD-9 to 69,000 in ICD-10. Moreover, the number of hospital inpatient procedure codes will escalate from 3800 to 72,000.5 The enhanced specificity and increased number of diagnostic codes with ICD-10 allow a more robust capture of the context of the clinical note (medical record), including the traditional subjective, objective, assessment plan (SOAP). The clinical data in the record are matched with an ICD diagnostic code or codes. With ICD-10, there are now lateralization and pinpointing of the diagnostic disease entities, such as diabetes, diabetic foot ulcer, and to the region of the body and/or anatomic site (See Clinical Management Extra, page 84). The “forced change to ICD-10” is like most significant changes in healthcare; 2 camps emerge—for and against! According to the Centers for Medicare & Medicaid Services (CMS), the American Health Information Management Association, and other proponents of implementing ICD-10, the system may improve patient outcomes through the use of robust health information technology applications in research, population health, payment, and healthcare economics. All of these attributes are in line with the managed care philosophy of the Affordable Care Act, signed into law by President Obama in March 2010. Those against ICD-10, including the American Medical Association (AMA), support a delay in its implementation by citing cost and burdensome health information technology systems, especially coming on the heels of meeting the basic meaningful use of electronic medical records mandated by CMS. According to a study by the AMA, the cost per physician practice to implement ICD-10 includes training, vendor and software upgrades, testing, and payment disruption. These costs could be as much as $8 million for large physician practices and approximately $225,000 for smaller practices.6 The move is expected to be “much more disruptive for physicians” than previous mandates. Other detractors include the Texas Medical Association; according to Dr Joseph Schneider,7 “ICD-10 is already 25 years old,” and “it predates modern health technology.” There are even suggestions that “ICD-11” should be developed with advances in modern health information technology in mind.7 To top off the controversy, Congress may have the last word on whether ICD is postponed beyond the new implementation date of October 1, 2015.FigureRichard “Sal” Salcido, MD, EdD

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.067
GPT teacher head0.486
Teacher spread0.418 · 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