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Record W2791433711 · doi:10.1177/1129729818762008

Validity of administrative database code algorithms to identify vascular access placement, surgical revisions, and secondary patency

2018· article· en· W2791433711 on OpenAlexafffundabout
Ahmed A. Al‐Jaishi, Louise Moist, Matthew J. Oliver, Danielle M. Nash, Jamie L Fleet, Amit X. Garg, Charmaine E. Lok

Bibliographic record

VenueThe Journal of Vascular Access · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsHealth Sciences CentreToronto General HospitalUniversity Health NetworkMcMaster UniversitySunnybrook Health Science CentreWestern UniversityInstitute for Clinical Evaluative Sciences
FundersInstitute for Clinical Evaluative Sciences
KeywordsAlgorithmMedicinePredictive valueConfidence intervalVascular accessDatabaseComputer scienceSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND:: We assessed the validity of physician billing codes and hospital admission using International Classification of Diseases 10th revision codes to identify vascular access placement, secondary patency, and surgical revisions in administrative data. METHODS:: We included adults (≥18 years) with a vascular access placed between 1 April 2004 and 31 March 2013 at the University Health Network, Toronto. Our reference standard was a prospective vascular access database (VASPRO) that contains information on vascular access type and dates of placement, dates for failure, and any revisions. We used VASPRO to assess the validity of different administrative coding algorithms by calculating the sensitivity, specificity, and positive predictive values of vascular access events. RESULTS:: The sensitivity (95% confidence interval) of the best performing algorithm to identify arteriovenous access placement was 86% (83%, 89%) and specificity was 92% (89%, 93%). The corresponding numbers to identify catheter insertion were 84% (82%, 86%) and 84% (80%, 87%), respectively. The sensitivity of the best performing coding algorithm to identify arteriovenous access surgical revisions was 81% (67%, 90%) and specificity was 89% (87%, 90%). The algorithm capturing arteriovenous access placement and catheter insertion had a positive predictive value greater than 90% and arteriovenous access surgical revisions had a positive predictive value of 20%. The duration of arteriovenous access secondary patency was on average 578 (553, 603) days in VASPRO and 555 (530, 580) days in administrative databases. CONCLUSION:: Administrative data algorithms have fair to good operating characteristics to identify vascular access placement and arteriovenous access secondary patency. Low positive predictive values for surgical revisions algorithm suggest that administrative data should only be used to rule out the occurrence of an event.

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)0.038
metaresearch head score (Gemma)0.195
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.195
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.421
GPT teacher head0.557
Teacher spread0.136 · 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.

Study designObservational
DomainMethods
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

Citations7
Published2018
Admission routes3
Has abstractyes

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