A knowledge synthesis of health research reporting standards relevant to epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have examined epilepsy surgery outcomes, yet the variability in the level of detail reported hampers our ability to apply these findings broadly across patient groups. Established reporting standards in other clinical research fields enhance the quality and generalizability of results, ensuring that the insights gained from studying these surgeries can benefit future patients effectively. This study aims to assess current reporting standards for epilepsy surgery research and identify potential gaps and areas for enhancement. METHODS: The Enhancing the Quality and Transparency of Health Research (EQUATOR) repository was accessed from inception to April 27, 2023, yielding 561 available reporting standards. Reporting standards were manually reviewed in duplicate independently for applicability to epilepsy and/or neurosurgery research. The reporting standards had to cover the following aspects in human studies: (1) reporting standards for epilepsy/epilepsy surgery and (2) reporting standards for neurosurgery. Disagreements were resolved by a third author. The top five neurosurgery, neurology, and medicine journals were also identified through Google Scholar's citation index and examined to determine the relevant reporting standards they recommended and whether those were registered with EQUATOR. RESULTS: Of the 561 EQUATOR reporting standards, 181 were pertinent to epilepsy surgery. One was related to epilepsy, six were specific to surgical research, and nine were related to neurological/neurosurgical research. The remaining 165 reporting standards were applicable to research across various disciplines and included but were not limited to CONSORT (Consolidated Standards of Reporting Trails), STROBE (Strengthening the Reporting of Observational Studies in Epidemiology), and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). None of these required reporting factors associated with epilepsy surgery outcomes, such as duration of epilepsy or magnetic resonance imaging findings. SIGNIFICANCE: Reporting standards specific to epilepsy surgery are lacking, reflecting a gap in standards that may affect the quality of publications. Improving this gap with a set of specific reporting standards would ensure that epilepsy surgery studies are more transparent and rigorous in their design.
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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 | 0.670 | 0.889 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.083 | 0.071 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".