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Outcome of patients treated with cobalt and 6 MV in head and neck cancers

2001· article· en· W2160982912 on OpenAlexaff
A Bonneau Fortin, Jos�e Allard, Michele Albert, Jean Roy

Bibliographic record

VenueHead & Neck · 2001
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsHôtel-Dieu de Québec
Fundersnot available
KeywordsMedicineRadiation therapyHead and neck cancerSurgeryHead and neckCobaltRetrospective cohort studyBolus (digestion)

Abstract

fetched live from OpenAlex

BACKGROUND: Since 1992, we have been using a 6-MV linear accelerator instead of a cobalt machine. The aim of the study is to evaluate the impact of this on neck control, particularly on postoperative patients in which subcutaneous tissues are at risk. METHOD: A retrospective study including all of 1,452 consecutive patients treated by definitive or postoperative radiotherapy between 1989 and 1997. All stages and subsites of the head/neck were included. Local and neck control were evaluated by the Kaplan Meier method, and comparisons were made between the cobalt and the 6-MV subgroups with a subsequent Cox analysis. For neck control analysis, the postoperative patients were divided in low and high risk (extracapsular extension [ECE], >two nodes, or T4). RESULTS: Radical radiotherapy: A better local control (LC) is observed with 6 MV than with cobalt, but neck control was similar. Postoperative radiotherapy: A better LC is observed with 6 MV. In high-risk patients, the neck control was higher for the cobalt group (79%) vs 60% for the 6-MV group (p = .09 and .03 in a Cox model). CONCLUSION: In postoperative patients at high risk for neck relapse, cobalt seems to give a better neck control. We are currently doing a prospective study in which a bolus is added for half the treatment when patients at high risk for neck relapse are treated with 6 MV.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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 designObservational
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

Citations8
Published2001
Admission routes1
Has abstractyes

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