308 What's keeping you awake at night? A study of pain trendography
Bibliographic record
Abstract
Background: Pain is a common symptom and may be subject to chronobiological variation. With the advent of smart phones and rapid accessibility to internet, the ability to search is fully integrated into everyday life and could be a real-time representation the occurrence of a medical symptom. Trends is an online tool providing real-time search data which can be analysed according to time period and location to provide information on epidemiological phenomena. This instrument has been used to map viral outbreaks and chronic diseases and has been recognised as a rich source of epidemiological data by the Institute of Medicine in the United States. We aimed to assess the diurnal variation in online searches in the United Kingdom (UK) for pain using the Trends tool and investigate whether the patterns observed were replicated globally. Methods: The word pain and related searches including analgesic medications, were assessed for seven-day variation using the Trends tool. The search term pain and related terms were assessed for seven-day variation in other English-searching countries including UK, Ireland, United States, Canada, South Africa, Australia, and New Zealand. Correlations between search trends were analysed using Spearman’s rank. A list of the most popular US search terms was utilised as a control group of searches.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".