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Development and validation of a prediction-score model for distant metastases in major salivary gland carcinoma.

2019· article· en· W2946996357 on OpenAlexaff
Jelena Lukovic, Fatima Alfaraj, Michelle Mierzwa, Gustavo Nader Marta, Wei Xu, Jie Su, Fábio Ynoe de Moraes, Shao Hui Huang, Scott V. Bratman, Brian O’Sullivan, John Kim, Jolie Ringash, John Waldron, John R. de Almeida, David P. Goldstein, Andrew J. Rosko, M.E. Spector, Luiz Paulo Kowalski, Andrew Hope, Ali Hosni

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsBC Cancer AgencyPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCohortLymphovascular invasionInternal medicineCumulative incidenceIncidence (geometry)Proportional hazards modelOncologyFramingham Risk ScoreCancerDiseaseMetastasis

Abstract

fetched live from OpenAlex

6085 Background: We developed and validated a prediction-score for distant metastases (DM) in major salivary gland carcinoma (SGC). Methods: Patients with SGC treated with curative-intent surgery +/- postoperative radiation therapy (PORT) at 4 tertiary cancer centers were divided into discovery (institution A&B) and validation (institution C&D) cohorts. Multivariable analysis using competing risk regression was used to identify predictors of DM in the discovery cohort and create a prediction score. The optimal score cut-off for high vs low-DM risk was determined using a minimal p-value approach. The results were subsequently evaluated in the validation cohort. The cumulative incidence and Kaplan-Meier methods were used to analyze DM and overall survival (OS), respectively. Results: Overall, 1035 patients were included (Table). In the discovery cohort, DM predictors (risk score coefficient) were: positive margin (0.6), pT3-4 (0.7), pN+ (0.7), lymphovascular invasion (LVI; 0.8), and high risk histology* (1.2). High DM-risk SGC was defined by sum of coefficients greater than 2. In the discovery cohort, the 5-year cumulative incidence of DM for high vs low risk SGC was 50% vs 8%; p < 0.01; these results were similar in the validation cohort (44% vs 4% at 5 years; p < 0.01). In the combined cohorts, this model predicted distant-only failure (40% vs 6%, p < 0.01) and late ( > 2yr post surgery) DM (22% vs 4%; p < 0.01). Patients with high DM-risk SGC had an increased incidence of DM in the subgroup receiving PORT (46% vs 8%; p < 0.01) or concurrent chemotherapy (71% vs 34%; p < 0.01). The 5-yr OS for high vs low risk SGC was 48% vs 92% (p < 0.01). Conclusions: This validated prediction score model may be used to identify SGC patients at increased risk for DM and select those who may benefit from prospective evaluation of treatment intensification and/or surveillance strategies. Baseline characteristics. [Table: see text]

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.133
GPT teacher head0.415
Teacher spread0.282 · 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 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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