Evaluation of txt2MEDLINE and Development of Short Messaging Service–Optimized, Clinical Practice Guidelines in Botswana
Bibliographic record
Abstract
OBJECTIVE: Currently clinicians in sub-Saharan Africa have limited access to the Internet, whereas mobile phone access and use is extensive. The University of Pennsylvania in collaboration with the National Library of Medicine launched txt2MEDLINE, a short messaging service (SMS) query of PubMed/MEDLINE, and SMS-optimized clinical guidelines in Botswana. The objective of this project was to establish and evaluate the utility of these tools for clinicians in Botswana. MATERIALS AND METHODS: A local server was established at the University of Botswana that allowed clinicians to send queries and receive results via local (in-country) SMS text messaging on any type of cellular phone. The queries sent via txt2MEDLINE were returned as abbreviated "the bottom line" summaries of abstracts. The 2007 Botswana Treatment Guide was converted into a format that can be queried by SMS. Various types of healthcare workers were recruited to use and evaluate these services. RESULTS: Seventy-six healthcare workers attended training sessions for these services. In the preusage survey, most said they would use the services daily or weekly. During a 4-week trial period, use of these services dropped off dramatically. Participant feedback was collected and indicated that improvements in ease of use would increase the usage. CONCLUSIONS: This pilot project enables clinicians to query and receive PubMed abstract summaries and country-specific clinical guidelines using mobile phones. Feedback offers insight on how to improve this technology so that it can be adopted for long-term use. With further adjustments, these resources may provide an effective working model for other countries where limited Internet access impedes upon patient care.
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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".