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Record W2107172893 · doi:10.1120/jacmp.v15i4.4893

Assessing the deviation from the inverse square law for orthovoltage beams with closed‐ended applicators

2014· article· en· W2107172893 on OpenAlexaff
James L Gräfe, Yannick Poirier, Ferenc Jacso, Rao Khan, H.P. Liu, J. Eduardo Villarreal‐Barajas

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsRed Deer PolytechnicUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsInverse-square lawDivergence (linguistics)ComputationSquare (algebra)InverseBeam divergenceStandard deviationBeam (structure)Field sizeOpticsScatteringPhysicsField (mathematics)MathematicsGeometryStatisticsBeam diameterAlgorithmClassical mechanicsLaser beams

Abstract

fetched live from OpenAlex

In this report, we quantify the divergence from the inverse square law (ISL) of the beam output as a function of distance (standoff) from closed-ended applicators for a modern clinical orthovoltage unit. The divergence is clinically significant exceeding 3% at a 1.2 cm distance for 4 × 4 and 10 × 10 cm2 closed-ended applicators. For all investigated cases, the measured dose falloff is more rapid than that predicted by the ISL and, therefore, causes a systematic underdose when using the ISL for dose calculations at extended SSD. The observed divergence from the ISL in closed-ended applicators can be explained by the end-plate scattering contribution not accounted for in the ISL calculation. The standoff measurements were also compared to the predictions from a home-built kV dose computation algorithm, kVDoseCalc. The kVDoseCalc computation predicted a more rapid falloff with distance than observed experimentally. The computation and measurements agree to within 1.1% for standoff distances of 3 cm or less for 4 × 4 cm2 and 10 × 10 cm2 field sizes. The overall agreement is within 2.3% for all field sizes and standoff distances measured. No significant deviation from the ISL was observed for open-ended applicators for standoff distances up to 10 cm.

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 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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.361
Teacher spread0.334 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations28
Published2014
Admission routes1
Has abstractyes

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