Topographical disorientation in a patient who never developed navigational skills: The (re)habilitation treatment
Bibliographic record
Abstract
Although many cases of topographical disorientation are described in the literature, very few attempts have been made to rehabilitate this deficit, most likely because it is a multi-faceted syndrome in which different patients are affected by different topographical deficits. Therefore, it is not easy to develop a single rehabilitation programme to improve all types of topographical disorders. We describe the rehabilitation of a young woman with selective and pervasive topographical disorientation who never developed navigational skills due to a cerebral malformation bilaterally involving the retrorolandic regions. During treatment, the patient was trained to explore her surroundings carefully, to orient herself and then to move in the environment using a language-based strategy. At the end of the treatment, the patient was able to navigate in the environment by adopting several cognitive strategies useful for orientation. This result was maintained at the one-year follow-up, at which time the patient was also able to reach locations she had never been to alone. These results suggest that even patients who have never developed the ability to orient themselves in the environment can still achieve very good functional recovery if they are accurately assessed and submitted to a specific rehabilitation intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".