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Record W4313361678 · doi:10.3171/2022.10.jns22753

Establishing ranked priorities for future hydrocephalus research

2022· article· en· W4313361678 on OpenAlexaff
Noriana E. Jakopin, Elliot Myong, Trish Bogucki, Diana D. Gray, Paul Gross, J. Gordon McComb, Chevis N. Shannon, Mandeep S. Tamber, Maiko Toyama, Tessa Van der Willigen, Amirmehdi Yazdani, Mark G. Hamilton, Jenna E. Koschnitzky

Bibliographic record

VenueJournal of neurosurgery · 2022
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsPrioritizationHydrocephalusMedicineAllianceProcess managementPolitical scienceSurgeryBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this initiative was to develop a ranked list of hydrocephalus research priorities as determined by the hydrocephalus patient community in conjunction with the healthcare and scientific community. METHODS: Using the validated methodology published by the James Lind Alliance (JLA), the Hydrocephalus Association (HA) administered two surveys and hosted a final prioritization workshop. Survey One solicited open-ended responses from the community. From these responses, a long list of priority statements was developed. This list was then consolidated into a short list of research priority statements, which, after a nonsystematic literature review, were verified as being research uncertainties. Survey Two asked the community members to select their top 10 priorities from the short list. The final prioritization leading to a final ranked top 20 list of hydrocephalus research priorities took place at a virtual workshop led by a team of trained facilitators, by means of an iterative process of consensus building. RESULTS: From Survey One, 3703 responses from 890 respondents were collected, leading to a long list of 146 priority statements. The consolidated short list contained 49 research priority statements, all of which were verified as uncertainties in hydrocephalus research. From an analysis of Survey Two responses, the top 21 research priority statements were determined. A consensus on these statements was reached at the virtual workshop, leading to a final ranked top 20 list of hydrocephalus research priorities, within which needs were apparent in several areas: development of noninvasive and/or one-time therapies, reduction of the burden of current treatments, improvement of the screening and diagnosis of hydrocephalus, improved quality of life, and improved access to care. CONCLUSIONS: By gathering extensive input from the hydrocephalus community and using an iterative process of consensus building, a ranked list of the top 20 hydrocephalus research priorities was developed. The HA will use this ranked list to guide future research programs and encourages the healthcare and scientific community to do the same.

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 imitation

Not 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.

metaresearch head score (Codex)0.143
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.143
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0150.007
Science and technology studies0.0060.002
Scholarly communication0.0140.011
Open science0.0040.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.095
GPT teacher head0.343
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2022
Admission routes1
Has abstractyes

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