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Reviewing systemic treatment regimens to reduce unintentional errors.

2017· article· en· W2604924954 on OpenAlexaffabout
Andrea Crespo, Erin Redwood, Kathy Vu, Vishal Kukreti

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineRegimenMultidisciplinary approachQuality (philosophy)HarmMedical emergencyIntensive care medicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

51 Background: Use of systemic treatment computerized prescriber order entry (ST CPOE) and pre-printed orders (PPO) are proven error reduction measures. Such systems are not failsafe, as regimen development depends on cognitive input at critical points and is susceptible to human error. No known guidance currently exists in oncology to ensure regimens are of high quality and built as intended. The purpose of this initiative was to improve the quality of oncology regimens in Ontario. Methods: A review of 35 centres, representing 75 treatment centres in a hub-and-spoke model, was conducted for all active regimens (PPO and CPOE) to ensure they were built as intended, with respect to drugs and doses. Centres completed an exploratory survey to report any unintentional discrepancies and existing maintenance review processes. The survey collected centre demographics and contained descriptive questions to document details of the regimen review and maintenance processes. Results: The review yielded an 86% response rate (12 regional and 18 community cancer centres). Upwards of 700 regimens were reviewed by a multidisciplinary team at each participating centre. Unintentional discrepancies were reported by 7 of the 30 (23%) centres (range of 2 – 141 per centre); types and examples are presented in the Table. Only 2 of 30 centres (7%) had an established regimen maintenance process. Conclusions: The review identified unintentional discrepancies and, due to the potential for patient harm, corrective action has been taken. There is a need for guidance and adoption of a standardized approach in order to sustain a high-quality regimen build and review process across the province. Consensus-based recommendations for ST CPOE and PPO regimen development and maintenance have been developed. Identified discrepancies have been amended and maintenance review processes are now implemented to improve the quality and safety of systemic treatment delivery. [Table: see text]

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)0.040
metaresearch head score (Gemma)0.167
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: Review · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.167
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.829
GPT teacher head0.638
Teacher spread0.191 · 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
GenreReview

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
Published2017
Admission routes2
Has abstractyes

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