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Record W2883325723 · doi:10.1097/ccm.0000000000003320

Handovers Among Staff Intensivists: A Study of Information Loss and Clinical Accuracy to Anticipate Events*

2018· article· en· W2883325723 on OpenAlex
Mariana V. Monteiro, Karina Braga Ribeiro, Guilherme Schettino, André Carlos Kajdacsy-Balla Amaral

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Care Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMedical diagnosisMorningEmergency medicineAnticipation (artificial intelligence)Observational studyProspective cohort studyPatient safetyMedical emergencyPediatricsInternal medicineHealth care

Abstract

fetched live from OpenAlex

OBJECTIVES: Handovers are associated with medical errors, and our primary objective is to identify missed diagnosis and goals immediately after a shift handover. Our secondary objective is to assess clinicians' diagnostic accuracy in anticipating clinical events during the night shift. DESIGN: Single-center prospective observational cohort study. SETTING: Thirty-bed tertiary ICU in Sao Paulo, Brazil. PATIENTS: Three-hundred fifty-two patient encounters over 44 day-to-night handovers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used a multimethods approach to measure transmission of information among staff physicians on diagnoses and goals for the night shift. We surveyed clinicians immediately after a handover and identified clinical events through chart abstractions and interviews with clinicians the next morning. Nighttime clinicians correctly identified 454 of 857 diagnoses (53%; 95% CI 50-56) and 123 of 304 goals (40%; 95% CI, 35-46). Daytime clinicians were more sensitive (65% vs 46%; p < 0.01) but less specific (82% vs 91%; p < 0.01) than nighttime clinicians in anticipating clinical events at night, resulting in similar accuracy (area under the receiver operating characteristic curve, 0.74 [95% CI, 0.68-0.79] vs 0.68 [95% CI 0.63-0.74]; p = 0.09). The positive predictive value of both daytime and nighttime clinicians was low (13% vs 17%; p = 0.2). Gaps in diagnosis and anticipation of events were more pronounced in neurologic diagnoses. CONCLUSIONS: Among staff intensivists, diagnoses and goals of treatment are either not conveyed or retained 50-60% of the cases immediately after a handover. Clinicians have limited ability to anticipate events, and the expectation that anticipatory guidance can inform handovers needs to be balanced against information overload. Handovers among staff intensivists showed more gaps in the identification of diagnostic uncertainty and for neurologic diagnoses, which could benefit from communication strategies such as cognitive checklists, prioritizing discussion of neurologic patients, and brief combined clinical examination at handover.

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)0.000
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.417
Teacher spread0.382 · 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