Perception and estimation of time in long-term survivors of childhood posterior fossa tumors
Bibliographic record
Abstract
We examined short duration perception (400 ms), long duration estimation (30 and 60 min), and spatiotemporal estimation in long-term survivors of childhood cerebellar tumors with a mean time since diagnosis of 14.2 years. Groups of individuals with tumors treated with surgery only (astrocytoma, N = 20) were compared to those with tumors treated with surgery, focal radiation, and craniospinal radiation (medulloblastoma, N = 20), and to age-matched controls (N = 40). Childhood lesions of the cerebellum produced enduring deficits in short-duration perception, but spared the ability to functionally estimate long durations, regardless of the pathology or treatment of the tumor. Evidence did not support any functional recovery over time of the cerebellar system that underlies short-duration perception. Younger age at treatment was not a protective factor. Although no group differences were present in the functional measures of long-duration estimation, tumor-related prospective memory deficits interfered with the ability to produce long-duration prospective estimates. The utilization of sensory and somatomotor information to refine real-world spatiotemporal estimates was compromised in the medulloblastoma group only.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".