Development and implementation of a novel metric score (<scp>SciMet</scp>) for scientific publications
Bibliographic record
Abstract
There is no formal or standard measure of quality, clinical impact, and scientific merit in the current literature in gynecologic oncology besides formal citation metrics. We aimed to develop and implement a multiparametric score to identify influential articles with transformative impact in gynecology oncology. We conducted a systematic search using PubMed from January 2010 to December 2022 for gynecologic oncology publications. Publications were ranked based on citations per year, top 100 were selected. After excluding 17, 83 original articles were included. A multiparametric score (SciMet) was developed to assess its relevance and impact considering citations per year, journal impact factor, study design, sample size, and altmetric attention score; numeric scores were assigned based on quartiles. This scoring system was applied to articles with citations per year values above the median (39 articles). Thirty-nine studies were analyzed based on the score. Number of CPY ranged from 42.7 to 261.2; journal IF, from 17.76 to 202.7; and sample size from 20 to 1 672 983. The total SciMet score ranged from 17 to 47 (median of 34). The top 10 included studies addressed screening interventions for ovarian cancer, PARP inhibitors and bevacizumab use as first-line maintenance in ovarian cancer, surgical trials in cervical cancer, and HPV vaccination impact on cervical intraepithelial neoplasia or cancer development. Articles with higher scores were mostly randomized clinical trials, and besides having high metrics, led to changes in management of gynecologic cancers, resulting in their incorporation into guidelines that shape established norms in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.059 | 0.044 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".