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Record W4396720055 · doi:10.1093/jnci/djae106

Response to Shamsi, Hina, Akhtar, et al.

2024· letter· en· W4396720055 on OpenAlexaff
Ryan S. Huang, Srinivas Raman

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2024
Typeletter
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

We sincerely thank Shamsi et al. for their comments on our article (1). Multidisciplinary cancer conferences provide a collaborative platform for cancer care, uniting specialists from diverse fields to enable a comprehensive evaluation of patients thereby promoting the development of more precise and tailored treatment strategies (2). In our meta-analysis of 134 287 patients, we found that multidisciplinary cancer conferences were associated with a statistically significantly increased overall survival across various cancer types. We agree with the authors that the integration of multidisciplinary cancer conferences is essential for the advancement of cancer treatment and advocate for the ongoing establishment of site-specific multidisciplinary tumor boards. Our study’s selection criteria only included comparative studies examining multidisciplinary cancer conference outcomes compared with nonmultidisciplinary cancer conference controls reporting overall survival data. Of the initial 3089 studies, many were excluded for not meeting these criteria, including noncomparative designs, lack of survival data, or a focus on administrative aspects of multidisciplinary cancer conferences. Covidence (Veritas Health Innovation, Melbourne, Australia), the tool we used for screening, is aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, which do not necessitate documenting exclusion reasons at the title and abstract phase; thus, we unfortunately cannot specify the count of exclusions by reason at this stage (3). However, of the 140 studies that passed title and abstract screening, 43 (31%) were excluded because of noncomparative study design, 26 (19%) for reporting outcomes other than overall survival, and 12 (9%) for their focus on the administrative facets of multidisciplinary cancer conferences. We appreciate the authors’ comments on the variation in the timing and frequency of multidisciplinary cancer conferences and acknowledge that they are crucial factors that can influence patient outcomes. Our study conducted subgroup analyses on studies where the multidisciplinary team met more than once to follow treatment beyond the initial discussion of treatment planning, and our results suggest that the positive effect on overall survival was maintained in this subgroup; further research is required to determine the optimal utilization of multidisciplinary cancer conferences in this setting. We also acknowledge the limitations inherent in the inclusion of retrospective studies in our analyses. Prospective studies are warranted to explore the dynamics of patient selection and referral to multidisciplinary cancer conferences, aiming to minimize potential biases and enhance the representativeness of patient populations. Additionally, research into the operational aspects of multidisciplinary cancer conferences, including meeting frequency and interdisciplinary communication, will be critical in maximizing the efficacy of these conferences. We remain committed to the continuous advancement of multidisciplinary care models and hope our work will serve as a catalyst for ongoing investigations ultimately leading to the establishment of global best practices for multidisciplinary cancer conferences that are adaptable across diverse health-care systems and patient populations. The data underlying this article are available in the article and in its online supplementary material. Ryan S. Huang, MSc, MD(C) (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing) and Srinivas Raman, MD, MASc, FRCPC (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing). None. RSH: None. SR: Institutional grant funding—Astra Zeneca, Knight therapeutics. Honoraria—Bayer, Astra Zeneca, Tersera, Sanofi, Verity pharma.

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)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.036
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0360.030
Insufficient payload (model declined to judge)0.0140.011

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 head0.105
GPT teacher head0.440
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2024
Admission routes1
Has abstractno

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