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Frequency and Clinical Importance of Pages Sent to the Wrong Physician

2009· letter· en· W2019043047 on OpenAlexaffabout
Brian M. Wong, Sherman Quan, C. Mark Cheung, Dante Morra, Peter G. Rossos, Khalil Sivjee, Robert Wu, Edward Etchells

Bibliographic record

VenueArchives of Internal Medicine · 2009
Typeletter
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

Frequency and Clinical Importance of Pages Sent to the Wrong Physician E ffective communication between health care pro- viders is essential to patient safety and quality of care. 1-6A retrospective study of 14 000 admissions found that communication failures were the most common cause of preventable disability or death and were nearly twice as common as those due to inadequate medical skill. 6A major type of communication failure is sending a page to the wrong physician.Prior studies have described paging problems such as paging the wrong physician, unanswered pages, and delayed responses but do not quantify the extent of the problem. 3Our primary aim was to quantify the frequency of pages sent to the wrong physician in 2 academic teaching hospitals and to examine the potential clinical importance of these errors.Methods.Sunnybrook Health Sciences Centre (SHSC) and the Toronto General Hospital (TGH) are tertiary care academic teaching hospitals affiliated with the

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)0.001
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.462
Teacher spread0.390 · 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.

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

Citations34
Published2009
Admission routes2
Has abstractyes

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