MANAGEMENT OF A GUILLAIN BARRE SYNDROME PATIENT THROUGH THREE TRACK REASONING: A CASE STUDY
Bibliographic record
Abstract
Background: Clinical reasoning is a thinking and decision making process which occur in clinical practice. It helps the health care providers to solve the clinical problem by using their reasoning process in an effective and efficient manner. Three track reasoning in one of the clinical reasoning process which includes the procedural, interactive and conditional reasoning to diagnose as well as ensure proper rehabilitation service according to patient and patient’s family members’ needs.Methods: A single case based study through the three track reasoning process. The purpose of this study was to explore the management strategies of a Gullian Barrie Syndrome (GBS) patient through three track reasoning. We have tried to show how the basic idea behind the reasoning process helped to determine the reasoning process and diagnosis. However it has performed through theory and observation. We have also showed how we used the reasoning process through with the common sense reasoning. However it was the part of procedural reasoning in three track clinical reasoning. In three track reasoning, there is also interactive and procedural reasoning part through which we told patient story about his condition, identified his and his family members expectations and to establish hypothesis as GBS. So three track reasoning also supported us to do reasoning process rather than selecting another reasoning process.Results: After analyzing the reasoning process it was identified that to be strict in a single reasoning process is very difficult. Clinical reasoning is the clinician’s ability through which they can consider the interpretation of different clinical findings. An expert clinician must have critical thinking skill rather than ignoring any symptoms or overemphasize the symptoms. In addition, patient’s knowledge, believes and reasoning was found an important part of clinical reasoning process in this study.Conclusion: We have been practicing clinical reasoning in our day to day practice, but we were not conscious about it. That’s why we may not critically think about it at the time of dealing with this case. Thus, selecting three track clinical reasoning model the case was diagnosed and treated accordingly.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".