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Applying the fragility index to randomized controlled trials evaluating total neoadjuvant therapy for rectal cancer.

2024· article· en· W4391095415 on OpenAlexaffabout
Tyler McKechnie, Kelly Brennan, Cagla Eskicioglu, Ameer Farooq, Sunil V. Patel

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsKingston Health Sciences CentreQueen's UniversityMcMaster University
Fundersnot available
KeywordsMedicineRandomized controlled trialColorectal cancerNeoadjuvant therapySample size determinationInternal medicineSurgeryCancerBreast cancerStatistics

Abstract

fetched live from OpenAlex

32 Background: A relatively novel summary measure that is not commonly reported in randomized controlled trials (RCTs) is the fragility index (FI). FI describes the number of additional events required for an outcome to lose statistical significance. It has been applied to a number of medical subspecialities such as critical care, orthopedic surgery and colorectal surgery. Recently, there has been significant interest in, and adoption of, total neoadjuvant therapy (TNT) for locally advanced rectal cancer (LARC). A number of RCTs have assessed TNT, but the robustness of these practice changing RCTs has never been evaluated. As such, we designed the present study to assess the robustness of the RCTs evaluating TNT for LARC using the FI. Methods: Relevant articles were identified through a recently published review article by Johnson et al. in the Canadian Journal of Surgery, that narratively reviewed all of the previously published RCTs evaluating TNT for LARC. We manually searched Google Scholar and PubMed to identify any other relevant RCTs. Outcomes within these RCTs that were either dichotomous outcomes or time to event outcomes were eligible for inclusion if the reported effect size had an associated p-value of less than 0.05. The main outcome was the FI for each statistically significant outcome. Walsh et al.’s method of calculating FI was utilized. A RCTs results were considered fragile if the FI was less than the loss to follow up for a given outcome. Correlations between FI and research characteristics were assessed using the Spearman’s rank correlation coefficients. Results: Ten RCTs were identified with 25 outcomes having statistically significant differences between groups (p-values < 0.05). Eleven outcomes were time-to-event outcomes, while the remainder were dichotomous outcomes. About half (n=13) were oncologic outcomes (i.e., survival, recurrence), while the rest (n=12) were short- and long-term complications. The median FI was 2 (interquartile range [IQR] 1-16). The number of patients lost to follow-up exceeded the FI in 17 outcomes (68.0%) and thus these results were considered “fragile”. Lower FI was associated with high risk of bias (rho=-0.5594) and higher loss to follow-up (i.e., greater than 5% vs. less than 5%) (rho=-0.4394), while higher FI was associated with large sample size (i.e., greater than 500 patients vs. less than 500 patients) (rho=0.5120). Conclusions: The robustness of outcomes from trials assessing TNT for LARC was found to be questionable. Most of these outcomes were fragile, as determined by the FI. In most cases, two or less additional events would have resulted in a loss of statistical significance of the reported results. Those using the results of these studies, including clinicians and health policy experts, should apply caution when interpreting these types of trials.

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)0.343
metaresearch head score (Gemma)0.702
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3430.702
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.029
Bibliometrics0.0270.023
Science and technology studies0.0010.004
Scholarly communication0.0080.007
Open science0.0040.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.644
GPT teacher head0.697
Teacher spread0.054 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations0
Published2024
Admission routes2
Has abstractyes

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