Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.09293.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:34:18.565347Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T10:40:17.677751Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 518cfd90-1366-4b3b-b291-4586af89b822 · inbound
Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity cites this paper.
Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:34:18.565347Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b46aaf7-21f7-4e08-aef6-36193b629fd5 · inbound
Critical Appraisal of Fairness Metrics in Clinical Predictive AI cites this paper.
Critical Appraisal of Fairness Metrics in Clinical Predictive AI A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:35:05.905266Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation abb18ffa-7fb5-4ddf-b743-c114294ee7e6 · inbound
A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes cites this paper.
A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T10:40:17.685106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.