Impact of hype on clinicians' evaluation of trials - a pilot study.
Bibliographic record
Abstract
Objective: The purpose of this study was to determine the practicality of using a teleconferencing platform to assess the effect of hype on clinicians' evaluations of reports of clinical trials in spinal care. Methods: Twelve chiropractic clinicians were interviewed via a videoconferencing application. Interviews were recorded and timed. Participant behaviour was monitored for compliance with the protocol. Differences between participants numerical ratings of hyped and non-hyped abstracts based on four measures of quality were analysed using pairwise comparisons (Wilcoxon signed rank test for independent samples). In addition, a linear mixed effects model was fitted with condition (i.e. hype vs. no hype) as a fixed effect and participant and abstract as random effects. Results: The interviews and data analysis were conducted without significant technical difficulty. Participant compliance was high, and no harms were reported. There were no statistically significant differences in the quality rankings of hyped versus non-hyped abstracts. Conclusion: The use of a videoconferencing platform to measure the effects of hype on clinicians' evaluations of abstracts of clinical trials is practical and an adequately powered study is justified. Lack of statistically significant results may well be due to low participant numbers.
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 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 | 0.227 | 0.546 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".