Descripción de la complejidad de los pacientes en la consulta externa de un Servicio de Cuidados Paliativos en un centro terciario: resultados preliminares
Bibliographic record
Abstract
Background: the best knowledge of the patients' complexity cared in the Palliative Care services is very important in order to provide to them the most appropriate allocation of resources. Regarding complexity the information is mainly from in-patients and patients cared at home, but no information is available on ambulatory patients. In this work we want to describe the complexity found in our out-patient clinic population. Material and method: we describe 54 consecutive patients attended in our out-patient clinic using some of the well-known parameters of clinical complexity found in their first visit. Results: median Karnofsky index was 70%. Past history of alcoholism was found in 30.5% and 24.5% had past history of psycho-pathological disturbances. Cognitive failure was present in the 32% of the patients. The most frequent symptoms were anxiety (83%), pain (77%) and depression (75%). Thirty - four per cent of the patient had a pain of difficult control accordingly to the Edmonton Staging System (ESS). Conclusions: our study shows that the population attended in our out-patient clinic meet the high-complexity criteria. We think that using such criteria can help to classify patients in order to offer to them the most appropriate resource to their needs.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".