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Record W4214526580 · doi:10.1515/dx-2018-0095

Diagnostic Error in Medicine

2018· article· en· W4214526580 on OpenAlexaff
Ruth Ryan, Jeff A. Brady, Sharon Cusanza, Robert El‐Kareh, Kelly T. Gleason, Penny Greenberg, Helen Haskell, Janice Kwan, Rebecca Jones, Ashley N. D. Meyer, Timothy J. Mosher, Andrew Olson, Art Papier, Susan Peterson, Dana Siegal, Robert L. Trowbridge, Laura Zwaan, Debra T. Choi, Li Wei, Daniel R. Murphy, Hardeep Singh, B. Gunsolus, K Litman, Mildred Hugh, Linda Fahey, S Becken, David E. Newman‐Toker, Najlla Nassery, A. C. Schaffer, C. Winnie Yu‐Moe, Ali S. Saber Tehrani, Gwendolyn Clemens, Zheng Wang, Mehdi Fanai, Frank J. Papa, Yalini Senathirajah, Elizabeth M. Borycki, André Kushniruk, Kenrick Cato

Bibliographic record

VenueDiagnosis · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

Background: Delays in cancer diagnosis can result from lack of timely follow-up of positive fecal occult blood tests (FOBT) and iron-deficiency anemia (IDA) for colorectal cancer (CRC) and elevated serum alpha-fetoprotein (AFP) levels for hepatocellular carcinoma (HCC). The VA implemented the patient-centered medical home (PCMH) model in 2010-2011 to provide patient-driven, team-based care with goals of improving healthcare outcomes. We hypothesized that PCMH will improve timely follow up of FOBT, IDA and AFP tests nationally in the VA. Methods: To identify patients with delayed follow-up after abnormal results, we applied previously validated electronic "trigger" algorithms to VA's national repository of electronic health record data. The trigger included patients with newly abnormal test results, excluding patients for whom follow-up was not required or action had been completed within 60 days of result. Positive predictive values of 57.0%, 55.1% and 82.3% for FOBT, IDA and AFP respectively, were higher than other known measures of diagnostic safety. We applied each trigger to all patients from 130 VA facilities across 18 national VA networks from 2006-2015. We derived yearly counts of trigger-positive patients based on VA facility and VA network to assess annual percent changes beginning in 2006. Negative binomial regression models were applied to assess overall and yearly changes in number of trigger-positive patients while accounting for clustering by VA network, over-dispersion and correlation due to repeated measures. An offset was created using expected number of trigger-positive patients by facility that adjusted for year and number of patients with primary care provider (PCP) visits that year. Final models were adjusted for VA network and PCP visits that year. Results: After excluding patients not meeting inclusion criteria, 5,887,006 and 37,762,419 patients had tests for FOBT and IDA from 2006-2015, respectively. Of patients who received FOBT tests, 245,776 patients met trigger-positive criteria and of those who received IDA tests, 303,323 met trigger-positive criteria. Similarly, 888,033 patients received AFP tests, with 12,098 patients meeting trigger-positive criteria. The trigger-positive count means for FOBT, IDA and AFP tests increased immediately following PCMH implementation, however the trigger-positive count means in subsequent years fluctuated between and among tests. Variability was also found by VA network, most likely due to facility size and complexity. Conclusion: Primary care medical home implementation does not appear to improve follow-up of abnormal test results that warrant cancer evaluation. Further contextual evaluation to explore lack of impact from this teamwork-based intervention is warranted.

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)0.163
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.163
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.055
GPT teacher head0.390
Teacher spread0.334 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2018
Admission routes1
Has abstractyes

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