Time series-based bibliometric analysis of a systematic review of multidisciplinary care for opioid dose reduction: exploring the origins of the North American opioid crisis
Bibliographic record
Abstract
Bibliometric analyses of systematic reviews offer unique opportunities to explore the character of specific scientific fields. In this time series-based analysis, dynamics of multidisciplinary care for chronic pain and opioid prescribing are analyzed over a forty-four year time span. Three distinct periods are identified, each defined by distinct research areas, as well as priorities regarding the use of opioids and the appropriate management of chronic pain. These scientometrically defined periods align with timelines identified previously by narrative historical accounts. Through cross-correlation with a mortality time series, a significant two-year lag between opioid overdose mortality and citation dynamics is identified between 2004 and 2019. This analysis demonstrates a bidirectional relationship between the scientific literature and the North American opioid overdose crisis, suggesting that the scientific literature is both reflective and generative of an important health and social phenomenon. A scientometric phenomenon of memory lapse, namely an overt and prolonged failure to cite older relevant literature, is identified using a metric of mean time to citation. It is proposed that this metric can be used to analyze changes in emerging literature and thus predict the nature of clinical and policy responses to the opioid crisis, and thus potentially to other health and social phenomena.
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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 | 0.041 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.099 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".