Agente conversacional para consultas sobre servicio médico en una clínica privada
Bibliographic record
Abstract
Conversational agents are programs that use natural language processing with a question and answer system. The research \naims to propose the implementation of a conversational agent for more frequent consultations in private health clinics. The \nconceptual framework is supported by the review and compilation of bibliographic references. English and Spanish from \nindexed journals, books, reports from national and international organizations, concerning the subject, in the same way, \nthe methodology is built with applications of observation sheets to the 16 private clinics in Ambato (Ecuador), interviews \nwith experts, research of software providers. As evidence of results, the advantages of using a conversational agent to solve \nmedical consultations are prioritized. For this reason, a methodological procedure was recommended for its implementation \nthat consists of 6 phases: 1. Analysis of potential client, 2. Selection of provider, 3 Selection of messaging platform, 4. \nInstallation and Configuration, 5. Training and tests, 6. Control and Evaluation.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".