A Case Review of Perceptual Deficit in PRES: Detailed Perceptual Evaluation is a Key to Definite Goal Achieving Techniques
Bibliographic record
Abstract
In an average adult, visual, perceptual and cognitive skills are highly developed. Following a cerebro vascular accident, because of the damage to the brain at its integrative level, certain perceptuocognitive deficits may result depending on whether the affectation is in only the right or left hemisphere or in both of them. These integrative affectations may be present in absence of any gross physical impairment in the form of Apraxias, Body scheme and Image deficits, Spatial relation syndrome, Agnosias or a combination. The resultant of these deficits is seen as difficulties in performing the ADL (Activities of Daily Living) to variable intensities. This affects not only the lifestyle of the individual but severely affects his confidence to be a productive member of the society. There are a variable battery of tests used for evaluation of these deficits that may or may not be standardized but are adequate for the clinician to understand the impact and specify the deficit so that the rehabilitative management is goal oriented and specific. Depending on the nature of the deficit is the treatment approach decided. The specific the rehabilitation technique used earlier is the independence gained. The present article discusses the use of OSOT (Ontario Society of Occupational Therapy) battery of test to evaluate the deficits and how the exact evaluation and understanding of the problem area would make the therapeutic intervention more definitive in terms of using appropriate technique for rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".