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Record W2961192469 · doi:10.1097/hp.0000000000001119

Streamlining Regulatory Activities Within Radiation Therapy Departments Using QATrack+

2019· article· en· W2961192469 on OpenAlex

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

VenueHealth Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsLicenseeService (business)Event (particle physics)Quality assuranceQuality (philosophy)Tracking (education)Control (management)Computer scienceWork (physics)Operations managementRisk analysis (engineering)Reliability engineeringBusinessEngineering

Abstract

fetched live from OpenAlex

Radiation therapy departments are faced with the challenge of tracking numerous quality control tests as well as monitoring service events affecting radiation therapy treatment units. Service events, in particular, pose a challenge since the clinic must be able to provide evidence to the regulatory body that both the service work and any required follow-up tests were recorded and authorized by the appropriate staff. This article presents an integrated approach to tracking quality control tests and service event logs using QATrack+. The newly developed version of this quality assurance software integrates quality control tracking with the service event log, allowing a direct link between a service event and any initiating routine tests or follow-up tests that are performed. This improves the ability of a licensee to ensure compliance with regulations and permits a simple platform from which to access all machine equipment tests and service events. Furthermore, this improves the ability of a department to assess the service record of equipment and to identify trends in failure modes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.342
Teacher spread0.316 · 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