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Diagnostic Coding for Epilepsy

2013· article· en· W4253833917 on OpenAlexaff
Jeffrey Buchhalter

Bibliographic record

VenueCONTINUUM Lifelong Learning in Neurology · 2013
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsCoding (social sciences)EpilepsyPsychologyBusinessPsychiatrySociology

Abstract

fetched live from OpenAlex

Clinicians commonly participate in three types of coding: (1) a code that characterizes the diagnosis of the patient, (2) a code that is intended for billing for evaluation and management (E/M coding), and (3) a code for a procedure, if performed.This discussion will focus on codes for diagnoses and procedures. DIAGNOSTIC CODING: BACKGROUNDThe diagnostic code set required by the Center for Medicare and Medicaid Services (CMS) and third-party payers for reimbursement is the International Classification of Diseases (ICD), administered in its primary form by the World Health Organization (WHO).In the United States, the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), released in 1979 is used, although International Classification of Diseases, 10th Revision (ICD-10) codes are used for death certificates.In addition to the necessity of the ICD code for reimbursement, these codes are used to assess the morbidity and mortality of diseases that directly influence public health policy, epidemiologic research, quality-of-care measures, and even emerging bioterrorism threats.For these reasons, accurate coding is an important physician obligation.Because of the expansion of medical knowledge in the past few decades, the codes provided by ICD-9 have proven to be inadequate.ICD-10 (released in 1994) was created to allow for greater diagnostic possibilities and is currently used in more than 100 countries around the world.The clinical modification of the 10th revision (ICD-10-CM) is scheduled to be implemented in the United States in October 2014.STRUCTURES OF ICD-9-CM AND ICD-10-CM: A BRIEF OVERVIEW ICD-9-CM characterizes disease entities with a 3-digit code followed by a decimal point and the possibility of 2 additional digits for greater specificity.Thus, 345 is the group for epilepsy and recurrent seizures, and the fourth digit (0 to 9) indicates the type of epilepsy or recurrent seizures (eg, generalized convulsive [345.1],grand mal status epilepticus [345.3],localization-related [345.4]).The fifth digit provides the opportunity to code if the seizure type or epilepsy is not intractable (0) or is intractable (1); for example, 345.11 is the code for generalized convulsive seizures that are intractable.

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)0.000
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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 integrity0.0000.001
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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