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Record W2770717910 · doi:10.4103/jpn.jpn_42_17

Health-related quality of life in children with congenital hydrocephalus and the parental concern: An analysis in a developing nation

2017· article· en· W2770717910 on OpenAlexaboutno aff
Monika Bawa, Jegadeesh Sundaram, Vedarth Dash, Nitin James Peters, K. L. N. Rao

Bibliographic record

VenueJournal of Pediatric Neurosciences · 2017
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)HydrocephalusMean valuePediatricsNuclear familyDemographySurgeryNursing

Abstract

fetched live from OpenAlex

PURPOSE: To analyze quality of life of children operated for congenital hydrocephalus and the concern of parents in taking care of these children. METHODS: Thirty patients who underwent ventriculo-peritoneal shunt were randomly selected with minimum gap of 1 year between surgery and study. Canadian validated questionnaire was used. Overall health score (OHS) and parental concern score (PCS) were correlated with gender, family type and number of surgeries. RESULTS: Mean OHS was 159.43 which was summation of physical health (mean 45.76), social-emotional (mean 80.03) and cognitive health scores (mean 33.66). Mean OHS was 151.57 for males and 177.77 for females (p-value 0.233). Nuclear and joint families had mean OHS of 160.36 and 158.89 respectively (p-value 0.944). Those who underwent one surgery had mean OHS of 167.48 and PCS of 23.10 whereas mean OHS was 140.66 and PCS was 27.78 for those with multiple procedures. Mean PCS for males was 26.71 and for females was 19.33 (p-value 0.036 statistically significant). This was not statistically significant between nuclear (24.73) and joint families (24.26). CONCLUSIONS: Quality of life of survivors of hydrocephalus is reasonably good even in developing countries due to keen parental involvement irrespective of gender, family type and number of surgeries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.323
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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