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In Support of a Journey of Metacognition

2011· article· en· W1875877400 on OpenAlexaff
Steven Friedman, Ray Guo

Bibliographic record

VenueAcademic Emergency Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineChartRecallMedical diagnosisMedical educationPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

I vividly recall my general surgery rotation as a medical student, about 20 years ago. My senior resident, BP, had a tremendous sense of humor, and early morning rounds—despite sleepless nights preceding—always promised a few laughs. While an excellent clinician and empathic communicator, BP preferred to round as early as possible, explaining that post-op patients would have fewer questions for the team at 6:00 AM than they might at 07:30 AM. A few weeks ago I bumped into BP in my neighborhood. I am now into my 15th year as attending emergency physician at a downtown teaching hospital, and he is a highly regarded academic pediatric head and neck surgeon. He fondly recalled the joking and predawn bedside rounds and then added, “Of course, a lot of this stops being funny once you become the attending physician.” In “To Keep an Incessant Watch,” the author has written a reflective article describing his residency-long exercise of comparing his working diagnoses for patients he referred for admission, with the final discharge diagnoses. The author writes, “By turning the act of signing a chart into an exercise in determining whether my impression was accurate, I was forced to learn from my successes and failures that would have otherwise remained uncovered.” While it is uncertain how much of a role this exercise played in the author’s improving diagnostic acumen, it is clear that the exercise stimulated reflection on his practice. The greatest benefit from this exercise, however, might be gleaned from stimulating reflection on cognition. Many of the near misses and errors identified by the author, at their essence, reflect cognitive dispositions to respond to specific clinical situations in typical bias-driven ways. The author laments dismissing an elderly patient’s painless rectal bleeding as internal hemorrhoids—ultimately diagnosed as rectal cancer—as a reminder of the shortcomings of his physical exam technique. However, reflection on cognition might suggest the centrality of anchoring bias and premature diagnostic closure as a cause. The author notes that he was jaded at times and describes sending home analgesic-seeking misanthropes who would later return and ultimately be diagnosed with conditions such as central cord compression. Being jaded—characteristic of all emergency medicine residents at some point—sets up the terrain for ascertainment bias (stereotyping) and fundamental attribution error. Croskerry writes,1“The unmasking of cognitive errors in the diagnostic process then allows for the development of debiasing techniques. This should be the ultimate goal, and it is not unrealistic.” Such strategies include developing insight and awareness, cognitive forcing strategies, and metacognition—essentially, thinking about the thinking process. The author laments other notorious error-prone practices. A preventable hyperkalemic arrest in a patient transferred from the ICU should remind the reader that transitions of care are a frequent source of error. Departing from standard practice of x-ray film review to expedite patient throughput is presented by the author as a deficiency in reading radiographs, but in reality exposes the hazards of deviating from standard practice when succumbing to extraneous pressures—time pressures, operational concerns, and authority gradients such as the need to meet or surpass perceived expectations of a superior.2 Other error-reducing strategies have been described for practice at the residence level. Gutierrez et al.3 advocate integrating the reporting of near misses into the shift signout routine, analyzing reported events, and incorporating error reporting into the medical school curriculum as early as first year. The addition of an online error reporting log as part of the resident signout routine has been described as a timely and convenient process to encourage error reporting.4 New training paradigms integrate deliberate attitudinal and behavioral changes focused on patient safety in a safe medically simulated environment.5 Last, mindfulness—perhaps the philosophical precursor of metacognition—will help empower the physician to restore clarity and balance. Buddhist philosopher Thich Nhat Hanh describes transforming feelings: “The first step in dealing with feelings is to recognize each feeling as it arises. The agent that does this is mindfulness. In the case of fear, for example, you bring out your mindfulness, look at your fear, and recognize it as fear.”6 The process that follows better equips the physician to recognize emotions—anxiety, anger, prejudice, fatigue—that can lead to bias in cognition and will lead to a calmer practitioner as well. The author’s reflective exercise warrants further development with a view toward incorporation into the residency experience. Central to this should be the process of thinking about thinking—reflecting not just on the decision made, but the processes and potential biases leading to that decision.

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)0.014
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0110.040
Scholarly communication0.0220.020
Open science0.0030.019
Research integrity0.0050.021
Insufficient payload (model declined to judge)0.0120.004

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 head0.158
GPT teacher head0.424
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2011
Admission routes1
Has abstractyes

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