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Record W2994891006 · doi:10.1503/cmaj.73407

Avoiding harm to patients: properly calculating costs of harm

2019· letter· en· W2994891006 on OpenAlexaffvenueabout
Robert Robson

Bibliographic record

VenueCanadian Medical Association Journal · 2019
Typeletter
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsHarmDo no harmComputer sciencePatient safetyRisk analysis (engineering)MedicineActuarial scienceMedical emergencyPsychologyHealth careBusinessPsychiatryEconomicsSocial psychology

Abstract

fetched live from OpenAlex

The article by Tessier and colleagues is an important step forward in understanding the financial as well as human impact of patient safety events in Canada.[1][1] One area that remains to be developed is our understanding and methods of calculating the financial consequences to the patient, family

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)0.017
metaresearch head score (Gemma)0.202
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.202
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.004
Scholarly communication0.0090.007
Open science0.0030.003
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0060.002

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 head0.038
GPT teacher head0.370
Teacher spread0.332 · 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".

Quick stats

Citations0
Published2019
Admission routes3
Has abstractyes

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