How data visualisation using historical medical journals can contribute to current debates around antibiotic use and antimicrobial resistance in primary care
Bibliographic record
Abstract
Background The early years of antibiotic use in primary care (c1950-1969) has received little attention. Medical journals provide a rich source for studying historic healthcare practitioners’ views and interests, with the potential to inform contemporary debate around issues of overuse and antimicrobial resistance. Aims Pilot study to test the application of digital methods to interrogate historical medical journal data in relation to antibiotic use. Methods / Approach Meta-data and scanned articles were extracted from the online British Journal of General Practice (BJGP) archive from inception (1953) to 1969. Searchable text was generated using an application called ABBYY optical character recognition, and Python used to generate data visualisations exploring (1) how BJGP changed during the period, (2) mentions of terms ‘antibiotic(s)’, ‘penicillin’, ‘resistance/resistant’ and mapping when and where they occurred. Results / Evaluation From 1953-1969, BJGP expanded in terms of number of annual issues (4 to 17) and annual pages (<25 to >1100). Heatmap visualisations were used to facilitate understanding of the frequency with which use of the term ‘antibiotic(s)’ occurred. By 1969 an article mentioning ‘antibiotic(s)’ was published monthly. Bigram searches found ‘treatment’ and ‘therapy’ to be the two most common terms that appeared with ‘antibiotic(s)’. The fourth and seventh most common terms were ‘resistant’ (first appearing in 1955) and ‘resistance’ (1962). Conclusions This pilot work shows that primary care publications increased considerably between 1953-1969. Articles on antibiotics featured frequently in relation to therapeutic intervention, and concerns around resistance occurred at an early stage. This approach provides new insights into how attitudes and behaviours around antibiotic use by primary care have evolved over time. It may also have the potential to inform study of the future use of antibiotics in primary care.
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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 | 0.033 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.035 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".