Clinical Care Pathways in Neurosurgery in the Canadian Context
Bibliographic record
Abstract
Background Clinical Care Pathways (CCPs) are a form of organized care processes and mutual decision making regarding select patient groups in a specific context. Their aim is to enhance care quality, patient satisfaction, and outcomes while optimizing safety and resource utilization. CCPs are poorly characterized in the Canadian context, with a few examples of successful programs but no organizational framework. Aims & Methods Through an interview series and qualitative descriptive content analysis, this thesis attempts to discern neurosurgeon perspectives on CCPs, important content and processes, and barriers to CCP development. Through a retrospective case control study, for operative patients at one Canadian center, a second project describes characteristics of entry to neurosurgical care for the purpose of understanding system inputs and subsequent CCP development. Results Interviewed neurosurgeons describe a positive sentiment toward CCPs overall, with nuanced understanding coalescing between numerous perspectives. Respondents described CCPs heterogeneously, but overall recognized their structure. Current care barriers were identified. Numerous existing informal or partial CCPs were discussed. CCPs are noted to have specific essential elements in their design. Retrospective review of care entry from July through December 2022 analyzed 654 operative cases and 2135 regional urgent consult requests. This analysis revealed differences in care entry dependent on disease entity, referral characteristics, patient characteristics, and patient acuity. Conclusions Design and development of CCPs is an emerging practice in the Canadian neurosurgical context. Numerous institutions and groups are presently developing their local CCPs. This analysis serves as a preliminary structure of CCP design, with an example retrospective analysis of the care entry component at a local institution.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".