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Record W4251015819 · doi:10.21203/rs.3.rs-29543/v1

Incidence of osteoradionecrosis of the jaws: A retrospective study of 620 head and neck cancer patients treated by radiotherapy

2020· preprint· en· W4251015819 on OpenAlexaff
Ryma Kabir, Robert Durand, David Roberge, Eric M. Đufresne, Phuc Félix Nguyen‐Tan, Matthieu Schmittbuhl

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOsteoradionecrosisMedicineRadiation therapyHead and neck cancerIncidence (geometry)Retrospective cohort studyHead and neckCancerDental extractionDentistrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background This study aims to assess systemic and local risk factors influencing the development of osteoradionecrosis of the jaws (ORN) and its incidence in head and neck cancer patients undergoing radiotherapy.Methods This was a retrospective cohort study of 620 adult patients following radiation for a head and neck cancer in 2011 or 2012. Results Of the 181 patients who did not require any extraction, the incidence of ORN was 0.5%. Of the 266 patients with 1491 dental extractions (mean 5.5 teeth per patient) performed before radiotherapy, the incidence of ORN was 3.7%. ORN was always observed in extraction sites located in the field of radiation. No dental extractions were done during radiotherapy. Of the 20 patients with 53 dental extractions (mean 2.7 teeth per patient) performed after radiotherapy, 15 teeth were located in the field of radiation. No case of ORN was reported in that group. For edentulous patients, ORN incidence was 1.7%. Conclusion Within the limitations of this study, the incidence of ORN can be minimized with a meticulous pre-radiotherapy dental examination, a comprehensive treatment plan and diligent post-radiotherapy follow-ups conducted by an experienced multidisciplinary team.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.004
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 head0.114
GPT teacher head0.481
Teacher spread0.367 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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