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Record W2085335774 · doi:10.1118/1.2962116

SU-GG-T-364: Development and Application of a Structural Quality Indicator for Radiotherapy Clinical Equipment

2008· article· en· W2085335774 on OpenAlexaboutno aff
Mehran Goharian, Peter Dunscombe, M Gackle, W. Mackillop

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceProtocol (science)Medical physicsDocumentationComputer scienceQuality (philosophy)Acceptance testingAuditReliability engineeringIdentification (biology)Test (biology)MedicineOperations managementSoftware engineeringExternal quality assessmentEngineeringAccounting

Abstract

fetched live from OpenAlex

Purpose: There is an increasing emphasis on quality indicators for documenting the performance of clinical radiotherapy equipment and processes. Such indicators, if relevant and unambiguous, can facilitate both external peer review audit (AAMP's- TG103) and internal review as a component of a quality improvement program. We present, for discussion, a graduated structural quality indicator to assess QC protocol compliance with existing performance standards and illustrate its use for linear accelerators and CT-simulators. Method and Materials: Our structural quality indicator is based on four features of the equipment QC protocol; tolerance level, action level, frequency and documentation of each test. The structural quality indicator is divided into five levels of compliance: full, substantial, partial, minimal, and non compliance. Each individual test in a local QC protocol can be assigned to one of the five categories using the proposed indicator. To evaluate the indicator for relevance, absence of ambiguity and ease of use, it was used to check the compliance of local Tom Baker Cancer Centre QC procedures (daily, monthly, and annually) against Canadian standards (www.medphys.ca) and the AAPM's-TG40 and TG66. Results: The results show that our Linac QC protocols were 82% and 88% in full or substantial compliance with CAPCA and AAPM's-TG40 respectively. However, in the case of the CT-simulator, full compliance was only 42% for CAPCA and 65% for AAPM's TG66. The form of the indicator facilitates the accurate identification of the discrepancies between the local protocol and the standard and hence guides remedial measures where necessary. Conclusion: A simple structural quality indicator for the evaluation of the documentary basis of a quality control program has been developed and applied. It is easy to use, relatively unambiguous and could form an objective component of a peer review exercise or a formal quality improvement program.

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)0.026
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
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 head0.034
GPT teacher head0.387
Teacher spread0.353 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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